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+{"cells":[{"cell_type":"markdown","id":"31969215-2a90-4d8b-ac36-646a7ae13744","metadata":{"id":"31969215-2a90-4d8b-ac36-646a7ae13744"},"source":["# Lab | Data Aggregation and Filtering"]},{"cell_type":"markdown","id":"a8f08a52-bec0-439b-99cc-11d3809d8b5d","metadata":{"id":"a8f08a52-bec0-439b-99cc-11d3809d8b5d"},"source":["In this challenge, we will continue to work with customer data from an insurance company. We will use the dataset called marketing_customer_analysis.csv, which can be found at the following link:\n","\n","https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\n","\n","This dataset contains information such as customer demographics, policy details, vehicle information, and the customer's response to the last marketing campaign. Our goal is to explore and analyze this data by first performing data cleaning, formatting, and structuring."]},{"cell_type":"code","source":["#Our goal is to explore and analyze this data by first performing data cleaning, formatting, and structuring.\n","import pandas as pd\n","url = 'https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv'\n","df = pd.read_csv(url)\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":739},"id":"M_MTyL43bFVS","executionInfo":{"status":"ok","timestamp":1760910183779,"user_tz":-120,"elapsed":1098,"user":{"displayName":"Rahala Moindze","userId":"02611134585983790642"}},"outputId":"076aaada-ee1a-4b73-9bc0-5cb9f2376fff"},"id":"M_MTyL43bFVS","execution_count":1,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       Unnamed: 0 Customer       State  Customer Lifetime Value Response  \\\n","0               0  DK49336     Arizona              4809.216960       No   \n","1               1  KX64629  California              2228.525238       No   \n","2               2  LZ68649  Washington             14947.917300       No   \n","3               3  XL78013      Oregon             22332.439460      Yes   \n","4               4  QA50777      Oregon              9025.067525       No   \n","...           ...      ...         ...                      ...      ...   \n","10905       10905  FE99816      Nevada             15563.369440       No   \n","10906       10906  KX53892      Oregon              5259.444853       No   \n","10907       10907  TL39050     Arizona             23893.304100       No   \n","10908       10908  WA60547  California             11971.977650       No   \n","10909       10909  IV32877         NaN              6857.519928      NaN   \n","\n","       Coverage Education Effective To Date EmploymentStatus Gender  ...  \\\n","0         Basic   College           2/18/11         Employed      M  ...   \n","1         Basic   College           1/18/11       Unemployed      F  ...   \n","2         Basic  Bachelor           2/10/11         Employed      M  ...   \n","3      Extended   College           1/11/11         Employed      M  ...   \n","4       Premium  Bachelor           1/17/11    Medical Leave      F  ...   \n","...         ...       ...               ...              ...    ...  ...   \n","10905   Premium  Bachelor           1/19/11       Unemployed      F  ...   \n","10906     Basic   College            1/6/11         Employed      F  ...   \n","10907  Extended  Bachelor            2/6/11         Employed      F  ...   \n","10908   Premium   College           2/13/11         Employed      F  ...   \n","10909     Basic  Bachelor            1/8/11       Unemployed      M  ...   \n","\n","       Number of Open Complaints Number of Policies     Policy Type  \\\n","0                            0.0                  9  Corporate Auto   \n","1                            0.0                  1   Personal Auto   \n","2                            0.0                  2   Personal Auto   \n","3                            0.0                  2  Corporate Auto   \n","4                            NaN                  7   Personal Auto   \n","...                          ...                ...             ...   \n","10905                        NaN                  7   Personal Auto   \n","10906                        0.0                  6   Personal Auto   \n","10907                        0.0                  2  Corporate Auto   \n","10908                        4.0                  6   Personal Auto   \n","10909                        0.0                  3   Personal Auto   \n","\n","             Policy  Renew Offer Type  Sales Channel  Total Claim Amount  \\\n","0      Corporate L3            Offer3          Agent          292.800000   \n","1       Personal L3            Offer4    Call Center          744.924331   \n","2       Personal L3            Offer3    Call Center          480.000000   \n","3      Corporate L3            Offer2         Branch          484.013411   \n","4       Personal L2            Offer1         Branch          707.925645   \n","...             ...               ...            ...                 ...   \n","10905   Personal L1            Offer3            Web         1214.400000   \n","10906   Personal L3            Offer2         Branch          273.018929   \n","10907  Corporate L3            Offer1            Web          381.306996   \n","10908   Personal L1            Offer1         Branch          618.288849   \n","10909   Personal L1            Offer4            Web         1021.719397   \n","\n","       Vehicle Class Vehicle Size Vehicle Type  \n","0      Four-Door Car      Medsize          NaN  \n","1      Four-Door Car      Medsize          NaN  \n","2                SUV      Medsize            A  \n","3      Four-Door Car      Medsize            A  \n","4      Four-Door Car      Medsize          NaN  \n","...              ...          ...          ...  \n","10905     Luxury Car      Medsize            A  \n","10906  Four-Door Car      Medsize            A  \n","10907     Luxury SUV      Medsize          NaN  \n","10908            SUV      Medsize            A  \n","10909            SUV      Medsize          NaN  \n","\n","[10910 rows x 26 columns]"],"text/html":["\n","  <div id=\"df-6c284c02-a54e-4126-8cd2-586e097a30db\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>Customer</th>\n","      <th>State</th>\n","      <th>Customer Lifetime Value</th>\n","      <th>Response</th>\n","      <th>Coverage</th>\n","      <th>Education</th>\n","      <th>Effective To Date</th>\n","      <th>EmploymentStatus</th>\n","      <th>Gender</th>\n","      <th>...</th>\n","      <th>Number of Open Complaints</th>\n","      <th>Number of Policies</th>\n","      <th>Policy Type</th>\n","      <th>Policy</th>\n","      <th>Renew Offer Type</th>\n","      <th>Sales Channel</th>\n","      <th>Total Claim Amount</th>\n","      <th>Vehicle Class</th>\n","      <th>Vehicle Size</th>\n","      <th>Vehicle Type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>DK49336</td>\n","      <td>Arizona</td>\n","      <td>4809.216960</td>\n","      <td>No</td>\n","      <td>Basic</td>\n","      <td>College</td>\n","      <td>2/18/11</td>\n","      <td>Employed</td>\n","      <td>M</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>9</td>\n","      <td>Corporate Auto</td>\n","      <td>Corporate L3</td>\n","      <td>Offer3</td>\n","      <td>Agent</td>\n","      <td>292.800000</td>\n","      <td>Four-Door Car</td>\n","      <td>Medsize</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>KX64629</td>\n","      <td>California</td>\n","      <td>2228.525238</td>\n","      <td>No</td>\n","      <td>Basic</td>\n","      <td>College</td>\n","      <td>1/18/11</td>\n","      <td>Unemployed</td>\n","      <td>F</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L3</td>\n","      <td>Offer4</td>\n","      <td>Call Center</td>\n","      <td>744.924331</td>\n","      <td>Four-Door Car</td>\n","      <td>Medsize</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>LZ68649</td>\n","      <td>Washington</td>\n","      <td>14947.917300</td>\n","      <td>No</td>\n","      <td>Basic</td>\n","      <td>Bachelor</td>\n","      <td>2/10/11</td>\n","      <td>Employed</td>\n","      <td>M</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>2</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L3</td>\n","      <td>Offer3</td>\n","      <td>Call Center</td>\n","      <td>480.000000</td>\n","      <td>SUV</td>\n","      <td>Medsize</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>XL78013</td>\n","      <td>Oregon</td>\n","      <td>22332.439460</td>\n","      <td>Yes</td>\n","      <td>Extended</td>\n","      <td>College</td>\n","      <td>1/11/11</td>\n","      <td>Employed</td>\n","      <td>M</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>2</td>\n","      <td>Corporate Auto</td>\n","      <td>Corporate L3</td>\n","      <td>Offer2</td>\n","      <td>Branch</td>\n","      <td>484.013411</td>\n","      <td>Four-Door Car</td>\n","      <td>Medsize</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>QA50777</td>\n","      <td>Oregon</td>\n","      <td>9025.067525</td>\n","      <td>No</td>\n","      <td>Premium</td>\n","      <td>Bachelor</td>\n","      <td>1/17/11</td>\n","      <td>Medical Leave</td>\n","      <td>F</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>7</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L2</td>\n","      <td>Offer1</td>\n","      <td>Branch</td>\n","      <td>707.925645</td>\n","      <td>Four-Door Car</td>\n","      <td>Medsize</td>\n","      <td>NaN</td>\n","    </tr>\n","    <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","      <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>10905</th>\n","      <td>10905</td>\n","      <td>FE99816</td>\n","      <td>Nevada</td>\n","      <td>15563.369440</td>\n","      <td>No</td>\n","      <td>Premium</td>\n","      <td>Bachelor</td>\n","      <td>1/19/11</td>\n","      <td>Unemployed</td>\n","      <td>F</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>7</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L1</td>\n","      <td>Offer3</td>\n","      <td>Web</td>\n","      <td>1214.400000</td>\n","      <td>Luxury Car</td>\n","      <td>Medsize</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>10906</th>\n","      <td>10906</td>\n","      <td>KX53892</td>\n","      <td>Oregon</td>\n","      <td>5259.444853</td>\n","      <td>No</td>\n","      <td>Basic</td>\n","      <td>College</td>\n","      <td>1/6/11</td>\n","      <td>Employed</td>\n","      <td>F</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>6</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L3</td>\n","      <td>Offer2</td>\n","      <td>Branch</td>\n","      <td>273.018929</td>\n","      <td>Four-Door Car</td>\n","      <td>Medsize</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>10907</th>\n","      <td>10907</td>\n","      <td>TL39050</td>\n","      <td>Arizona</td>\n","      <td>23893.304100</td>\n","      <td>No</td>\n","      <td>Extended</td>\n","      <td>Bachelor</td>\n","      <td>2/6/11</td>\n","      <td>Employed</td>\n","      <td>F</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>2</td>\n","      <td>Corporate Auto</td>\n","      <td>Corporate L3</td>\n","      <td>Offer1</td>\n","      <td>Web</td>\n","      <td>381.306996</td>\n","      <td>Luxury SUV</td>\n","      <td>Medsize</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>10908</th>\n","      <td>10908</td>\n","      <td>WA60547</td>\n","      <td>California</td>\n","      <td>11971.977650</td>\n","      <td>No</td>\n","      <td>Premium</td>\n","      <td>College</td>\n","      <td>2/13/11</td>\n","      <td>Employed</td>\n","      <td>F</td>\n","      <td>...</td>\n","      <td>4.0</td>\n","      <td>6</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L1</td>\n","      <td>Offer1</td>\n","      <td>Branch</td>\n","      <td>618.288849</td>\n","      <td>SUV</td>\n","      <td>Medsize</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>10909</th>\n","      <td>10909</td>\n","      <td>IV32877</td>\n","      <td>NaN</td>\n","      <td>6857.519928</td>\n","      <td>NaN</td>\n","      <td>Basic</td>\n","      <td>Bachelor</td>\n","      <td>1/8/11</td>\n","      <td>Unemployed</td>\n","      <td>M</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>3</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L1</td>\n","      <td>Offer4</td>\n","      <td>Web</td>\n","      <td>1021.719397</td>\n","      <td>SUV</td>\n","      <td>Medsize</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>10910 rows × 26 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c284c02-a54e-4126-8cd2-586e097a30db')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      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        0  DK49336     Arizona              4809.216960       No   \n","1               1  KX64629  California              2228.525238       No   \n","2               2  LZ68649  Washington             14947.917300       No   \n","3               3  XL78013      Oregon             22332.439460      Yes   \n","4               4  QA50777      Oregon              9025.067525       No   \n","...           ...      ...         ...                      ...      ...   \n","10905       10905  FE99816      Nevada             15563.369440       No   \n","10906       10906  KX53892      Oregon              5259.444853       No   \n","10907       10907  TL39050     Arizona             23893.304100       No   \n","10908       10908  WA60547  California             11971.977650       No   \n","10909       10909  IV32877         NaN              6857.519928      NaN   \n","\n","       coverage education effective_to_date employmentstatus gender  ...  \\\n","0         Basic   College           2/18/11         Employed      M  ...   \n","1         Basic   College           1/18/11       Unemployed      F  ...   \n","2         Basic  Bachelor           2/10/11         Employed      M  ...   \n","3      Extended   College           1/11/11         Employed      M  ...   \n","4       Premium  Bachelor           1/17/11    Medical Leave      F  ...   \n","...         ...       ...               ...              ...    ...  ...   \n","10905   Premium  Bachelor           1/19/11       Unemployed      F  ...   \n","10906     Basic   College            1/6/11         Employed      F  ...   \n","10907  Extended  Bachelor            2/6/11         Employed      F  ...   \n","10908   Premium   College           2/13/11         Employed      F  ...   \n","10909     Basic  Bachelor            1/8/11       Unemployed      M  ...   \n","\n","       number_of_open_complaints number_of_policies     policy_type  \\\n","0                            0.0                  9  Corporate Auto   \n","1                            0.0                  1   Personal Auto   \n","2                            0.0                  2   Personal Auto   \n","3                            0.0                  2  Corporate Auto   \n","4                            NaN                  7   Personal Auto   \n","...                          ...                ...             ...   \n","10905                        NaN                  7   Personal Auto   \n","10906                        0.0                  6   Personal Auto   \n","10907                        0.0                  2  Corporate Auto   \n","10908                        4.0                  6   Personal Auto   \n","10909                        0.0                  3   Personal Auto   \n","\n","             policy  renew_offer_type  sales_channel  total_claim_amount  \\\n","0      Corporate L3            Offer3          Agent          292.800000   \n","1       Personal L3            Offer4    Call Center          744.924331   \n","2       Personal L3            Offer3    Call Center          480.000000   \n","3      Corporate L3            Offer2         Branch          484.013411   \n","4       Personal L2            Offer1         Branch          707.925645   \n","...             ...               ...            ...                 ...   \n","10905   Personal L1            Offer3            Web         1214.400000   \n","10906   Personal L3            Offer2         Branch          273.018929   \n","10907  Corporate L3            Offer1            Web          381.306996   \n","10908   Personal L1            Offer1         Branch          618.288849   \n","10909   Personal L1            Offer4            Web         1021.719397   \n","\n","       vehicle_class vehicle_size vehicle_type  \n","0      Four-Door Car      Medsize          NaN  \n","1      Four-Door Car      Medsize          NaN  \n","2                SUV      Medsize            A  \n","3      Four-Door Car      Medsize            A  \n","4      Four-Door Car      Medsize          NaN  \n","...              ...          ...          ...  \n","10905     Luxury Car      Medsize            A  \n","10906  Four-Door Car      Medsize            A  \n","10907     Luxury SUV      Medsize          NaN  \n","10908            SUV      Medsize            A  \n","10909            SUV      Medsize          NaN  \n","\n","[10910 rows x 26 columns]"],"text/html":["\n","  <div id=\"df-880c10bf-7cff-4566-82d5-b5414d976b91\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>unnamed:_0</th>\n","      <th>customer</th>\n","      <th>state</th>\n","      <th>customer_lifetime_value</th>\n","      <th>response</th>\n","      <th>coverage</th>\n","      <th>education</th>\n","      <th>effective_to_date</th>\n","      <th>employmentstatus</th>\n","      <th>gender</th>\n","      <th>...</th>\n","      <th>number_of_open_complaints</th>\n","      <th>number_of_policies</th>\n","      <th>policy_type</th>\n","      <th>policy</th>\n","      <th>renew_offer_type</th>\n","      <th>sales_channel</th>\n","      <th>total_claim_amount</th>\n","      <th>vehicle_class</th>\n","      <th>vehicle_size</th>\n","      <th>vehicle_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>DK49336</td>\n","      <td>Arizona</td>\n","      <td>4809.216960</td>\n","      <td>No</td>\n","      <td>Basic</td>\n","      <td>College</td>\n","      <td>2/18/11</td>\n","      <td>Employed</td>\n","      <td>M</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>9</td>\n","      <td>Corporate Auto</td>\n","      <td>Corporate L3</td>\n","      <td>Offer3</td>\n","      <td>Agent</td>\n","      <td>292.800000</td>\n","      <td>Four-Door Car</td>\n","      <td>Medsize</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>KX64629</td>\n","      <td>California</td>\n","      <td>2228.525238</td>\n","      <td>No</td>\n","      <td>Basic</td>\n","      <td>College</td>\n","      <td>1/18/11</td>\n","      <td>Unemployed</td>\n","      <td>F</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L3</td>\n","      <td>Offer4</td>\n","      <td>Call Center</td>\n","      <td>744.924331</td>\n","      <td>Four-Door Car</td>\n","      <td>Medsize</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>LZ68649</td>\n","      <td>Washington</td>\n","      <td>14947.917300</td>\n","      <td>No</td>\n","      <td>Basic</td>\n","      <td>Bachelor</td>\n","      <td>2/10/11</td>\n","      <td>Employed</td>\n","      <td>M</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>2</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L3</td>\n","      <td>Offer3</td>\n","      <td>Call Center</td>\n","      <td>480.000000</td>\n","      <td>SUV</td>\n","      <td>Medsize</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>XL78013</td>\n","      <td>Oregon</td>\n","      <td>22332.439460</td>\n","      <td>Yes</td>\n","      <td>Extended</td>\n","      <td>College</td>\n","      <td>1/11/11</td>\n","      <td>Employed</td>\n","      <td>M</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>2</td>\n","      <td>Corporate Auto</td>\n","      <td>Corporate L3</td>\n","      <td>Offer2</td>\n","      <td>Branch</td>\n","      <td>484.013411</td>\n","      <td>Four-Door Car</td>\n","      <td>Medsize</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>QA50777</td>\n","      <td>Oregon</td>\n","      <td>9025.067525</td>\n","      <td>No</td>\n","      <td>Premium</td>\n","      <td>Bachelor</td>\n","      <td>1/17/11</td>\n","      <td>Medical Leave</td>\n","      <td>F</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>7</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L2</td>\n","      <td>Offer1</td>\n","      <td>Branch</td>\n","      <td>707.925645</td>\n","      <td>Four-Door Car</td>\n","      <td>Medsize</td>\n","      <td>NaN</td>\n","    </tr>\n","    <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","      <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>10905</th>\n","      <td>10905</td>\n","      <td>FE99816</td>\n","      <td>Nevada</td>\n","      <td>15563.369440</td>\n","      <td>No</td>\n","      <td>Premium</td>\n","      <td>Bachelor</td>\n","      <td>1/19/11</td>\n","      <td>Unemployed</td>\n","      <td>F</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>7</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L1</td>\n","      <td>Offer3</td>\n","      <td>Web</td>\n","      <td>1214.400000</td>\n","      <td>Luxury Car</td>\n","      <td>Medsize</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>10906</th>\n","      <td>10906</td>\n","      <td>KX53892</td>\n","      <td>Oregon</td>\n","      <td>5259.444853</td>\n","      <td>No</td>\n","      <td>Basic</td>\n","      <td>College</td>\n","      <td>1/6/11</td>\n","      <td>Employed</td>\n","      <td>F</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>6</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L3</td>\n","      <td>Offer2</td>\n","      <td>Branch</td>\n","      <td>273.018929</td>\n","      <td>Four-Door Car</td>\n","      <td>Medsize</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>10907</th>\n","      <td>10907</td>\n","      <td>TL39050</td>\n","      <td>Arizona</td>\n","      <td>23893.304100</td>\n","      <td>No</td>\n","      <td>Extended</td>\n","      <td>Bachelor</td>\n","      <td>2/6/11</td>\n","      <td>Employed</td>\n","      <td>F</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>2</td>\n","      <td>Corporate Auto</td>\n","      <td>Corporate L3</td>\n","      <td>Offer1</td>\n","      <td>Web</td>\n","      <td>381.306996</td>\n","      <td>Luxury SUV</td>\n","      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<td>0.0</td>\n","      <td>3</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L1</td>\n","      <td>Offer4</td>\n","      <td>Web</td>\n","      <td>1021.719397</td>\n","      <td>SUV</td>\n","      <td>Medsize</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>10910 rows × 26 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-880c10bf-7cff-4566-82d5-b5414d976b91')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","   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Use to_datetime without passing `errors` and catch exceptions explicitly instead\n","  pd.to_datetime(df[\"effective_to_date\"], errors=\"ignore\")\n","/tmp/ipython-input-2338632711.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n","  pd.to_datetime(df[\"effective_to_date\"], errors=\"ignore\")\n"]},{"output_type":"execute_result","data":{"text/plain":["2       2011-02-10\n","3       2011-01-11\n","10      2011-01-02\n","13      2011-01-26\n","16      2011-02-07\n","           ...    \n","10902   2011-01-24\n","10903   2011-01-07\n","10904   2011-02-24\n","10906   2011-01-06\n","10908   2011-02-13\n","Name: effective_to_date, Length: 4543, dtype: datetime64[ns]"],"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>effective_to_date</th>\n","    </tr>\n","  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datetime64[ns]</label>"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["df = df.dropna()\n","df.isnull().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":899},"id":"9FYlGbnncsSk","executionInfo":{"status":"ok","timestamp":1760910796161,"user_tz":-120,"elapsed":34,"user":{"displayName":"Rahala Moindze","userId":"02611134585983790642"}},"outputId":"33829dca-7534-4910-8bab-5e478da5646b"},"id":"9FYlGbnncsSk","execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["unnamed:_0                       0\n","customer                         0\n","state                            0\n","customer_lifetime_value          0\n","response                         0\n","coverage                         0\n","education                        0\n","effective_to_date                0\n","employmentstatus                 0\n","gender                           0\n","income                           0\n","location_code                    0\n","marital_status                   0\n","monthly_premium_auto             0\n","months_since_last_claim          0\n","months_since_policy_inception    0\n","number_of_open_complaints        0\n","number_of_policies               0\n","policy_type                      0\n","policy                           0\n","renew_offer_type                 0\n","sales_channel                    0\n","total_claim_amount               0\n","vehicle_class                    0\n","vehicle_size                     0\n","vehicle_type                     0\n","dtype: int64"],"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","     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  <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>vehicle_size</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>vehicle_type</th>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","id":"9c98ddc5-b041-4c94-ada1-4dfee5c98e50","metadata":{"id":"9c98ddc5-b041-4c94-ada1-4dfee5c98e50"},"source":["1. Create a new DataFrame that only includes customers who:\n","   - have a **low total_claim_amount** (e.g., below $1,000),\n","   - have a response \"Yes\" to the last marketing campaign."]},{"cell_type":"code","source":["#Create a new DataFrame that only includes customers who:\n","#have a low total_claim_amount (e.g., below $1,000),\n","#have a response \"Yes\" to the last marketing campaign.\n","df_filtered = df[(df[\"total_claim_amount\"] < 1000) & (df[\"response\"] == \"Yes\")]\n","df_filtered\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":652},"id":"m3SNAD9rd5iu","executionInfo":{"status":"ok","timestamp":1760910949694,"user_tz":-120,"elapsed":54,"user":{"displayName":"Rahala Moindze","userId":"02611134585983790642"}},"outputId":"7bbd3662-7684-4ae0-ca80-42d3dc1eef4b"},"id":"m3SNAD9rd5iu","execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       unnamed:_0 customer       state  customer_lifetime_value response  \\\n","3               3  XL78013      Oregon             22332.439460      Yes   \n","19             19  NJ54277  California              3746.751625      Yes   \n","69             69  QG27547      Oregon              2867.312197      Yes   \n","102           102  VG56765     Arizona              2471.528431      Yes   \n","113           113  EC28398      Oregon              5096.673223      Yes   \n","...           ...      ...         ...                      ...      ...   \n","10818       10818  XT35473     Arizona              2300.691547      Yes   \n","10825       10825  FB17016      Oregon              5470.060561      Yes   \n","10840       10840  ME22430      Nevada              2453.570794      Yes   \n","10887       10887  BY78730      Oregon              8879.790017      Yes   \n","10897       10897  MM70762     Arizona              9075.768214      Yes   \n","\n","       coverage             education effective_to_date employmentstatus  \\\n","3      Extended               College           1/11/11         Employed   \n","19     Extended               College           2/26/11         Employed   \n","69     Extended              Bachelor            1/3/11          Retired   \n","102       Basic  High School or Below           1/15/11         Employed   \n","113       Basic                Master           1/28/11         Disabled   \n","...         ...                   ...               ...              ...   \n","10818     Basic              Bachelor           1/15/11          Retired   \n","10825  Extended              Bachelor           2/17/11         Employed   \n","10840     Basic              Bachelor            2/9/11    Medical Leave   \n","10887     Basic  High School or Below            2/3/11         Employed   \n","10897     Basic                Master           1/26/11         Employed   \n","\n","      gender  ...  number_of_open_complaints number_of_policies  \\\n","3          M  ...                        0.0                  2   \n","19         F  ...                        1.0                  1   \n","69         F  ...                        0.0                  1   \n","102        M  ...                        0.0                  1   \n","113        F  ...                        0.0                  3   \n","...      ...  ...                        ...                ...   \n","10818      M  ...                        0.0                  1   \n","10825      F  ...                        0.0                  1   \n","10840      M  ...                        0.0                  1   \n","10887      F  ...                        0.0                  7   \n","10897      M  ...                        0.0                  8   \n","\n","          policy_type        policy  renew_offer_type  sales_channel  \\\n","3      Corporate Auto  Corporate L3            Offer2         Branch   \n","19      Personal Auto   Personal L2            Offer2    Call Center   \n","69      Personal Auto   Personal L3            Offer2    Call Center   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<td>10825</td>\n","      <td>FB17016</td>\n","      <td>Oregon</td>\n","      <td>5470.060561</td>\n","      <td>Yes</td>\n","      <td>Extended</td>\n","      <td>Bachelor</td>\n","      <td>2/17/11</td>\n","      <td>Employed</td>\n","      <td>F</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L3</td>\n","      <td>Offer2</td>\n","      <td>Agent</td>\n","      <td>702.990032</td>\n","      <td>SUV</td>\n","      <td>Medsize</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>10840</th>\n","      <td>10840</td>\n","      <td>ME22430</td>\n","      <td>Nevada</td>\n","      <td>2453.570794</td>\n","      <td>Yes</td>\n","      <td>Basic</td>\n","      <td>Bachelor</td>\n","      <td>2/9/11</td>\n","      <td>Medical Leave</td>\n","      <td>M</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L2</td>\n","      <td>Offer1</td>\n","      <td>Agent</td>\n","      <td>331.200000</td>\n","      <td>Four-Door Car</td>\n","      <td>Medsize</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>10887</th>\n","      <td>10887</td>\n","      <td>BY78730</td>\n","      <td>Oregon</td>\n","      <td>8879.790017</td>\n","      <td>Yes</td>\n","      <td>Basic</td>\n","      <td>High School or Below</td>\n","      <td>2/3/11</td>\n","      <td>Employed</td>\n","      <td>F</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>7</td>\n","      <td>Special Auto</td>\n","      <td>Special L2</td>\n","      <td>Offer1</td>\n","      <td>Agent</td>\n","      <td>528.200860</td>\n","      <td>SUV</td>\n","      <td>Small</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>10897</th>\n","      <td>10897</td>\n","      <td>MM70762</td>\n","      <td>Arizona</td>\n","      <td>9075.768214</td>\n","      <td>Yes</td>\n","      <td>Basic</td>\n","      <td>Master</td>\n","      <td>1/26/11</td>\n","      <td>Employed</td>\n","      <td>M</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>8</td>\n","      <td>Personal Auto</td>\n","      <td>Personal L1</td>\n","      <td>Offer1</td>\n","      <td>Agent</td>\n","      <td>158.077504</td>\n","      <td>Sports Car</td>\n","      <td>Medsize</td>\n","      <td>A</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>614 rows × 26 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed07e947-217f-41d4-9bd8-9cc598a81424')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 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Using the original Dataframe, analyze:\n","   - the average `monthly_premium` and/or customer lifetime value by `policy_type` and `gender` for customers who responded \"Yes\", and\n","   - compare these insights to `total_claim_amount` patterns, and discuss which segments appear most profitable or low-risk for the company."]},{"cell_type":"code","source":["#Using the original Dataframe, analyze:\n","#the average monthly_premium and/or customer lifetime value by policy_type and gender for customers who responded \"Yes\", and compare these insights to total_claim_amount patterns, and discuss which segments appear most profitable or low-risk for the company.\n","df_filtered = df[df[\"response\"] == \"Yes\"]\n","df_filtered.groupby([\"policy_type\", \"gender\"])[[\"monthly_premium_auto\", \"customer_lifetime_value\"]].mean()\n","\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"id":"vYyj3BPWeUVw","executionInfo":{"status":"ok","timestamp":1760911115037,"user_tz":-120,"elapsed":68,"user":{"displayName":"Rahala Moindze","userId":"02611134585983790642"}},"outputId":"390a2759-689a-48e6-eddd-3ef327f264b3"},"id":"vYyj3BPWeUVw","execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       monthly_premium_auto  customer_lifetime_value\n","policy_type    gender                                               \n","Corporate Auto F                  93.597403              8400.519583\n","               M                  90.819672              7545.446171\n","Personal Auto  F                  99.100000              7755.844201\n","               M                  92.821429              7402.677522\n","Special Auto   F                 102.294118              8403.318929\n","               M                  84.928571              8655.148804"],"text/html":["\n","  <div id=\"df-50a6d19c-0e46-4730-9c15-2332ee1a591d\" class=\"colab-df-container\">\n","    <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></th>\n","      <th>monthly_premium_auto</th>\n","      <th>customer_lifetime_value</th>\n","    </tr>\n","    <tr>\n","      <th>policy_type</th>\n","      <th>gender</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th rowspan=\"2\" valign=\"top\">Corporate Auto</th>\n","      <th>F</th>\n","      <td>93.597403</td>\n","      <td>8400.519583</td>\n","    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            total_claim_amount\n","policy_type    gender                    \n","Corporate Auto F               404.104008\n","               M               391.370774\n","Personal Auto  F               457.056315\n","               M               472.489180\n","Special Auto   F               495.689401\n","               M               368.523035"],"text/html":["\n","  <div id=\"df-fdd4d293-5969-48f4-b7fb-18a7d893a6ec\" class=\"colab-df-container\">\n","    <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></th>\n","      <th>total_claim_amount</th>\n","    </tr>\n","    <tr>\n","      <th>policy_type</th>\n","      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Analyze the total number of customers who have policies in each state, and then filter the results to only include states where there are more than 500 customers."]},{"cell_type":"code","source":["#Analyze the total number of customers who have policies in each state, and then filter the results to only include states where there are more than 500 customers.\n","df_filtered = df.groupby([\"state\"])[\"customer\"].count()\n","df_filtered[df_filtered > 500]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":209},"id":"rtoKsNJUfGQJ","executionInfo":{"status":"ok","timestamp":1760911207691,"user_tz":-120,"elapsed":33,"user":{"displayName":"Rahala Moindze","userId":"02611134585983790642"}},"outputId":"1211a818-8ca4-4f29-c124-7d07099c026b"},"id":"rtoKsNJUfGQJ","execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["state\n","Arizona        868\n","California    1552\n","Oregon        1281\n","Name: customer, dtype: int64"],"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","    </tr>\n","    <tr>\n","      <th>state</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Arizona</th>\n","      <td>868</td>\n","    </tr>\n","    <tr>\n","      <th>California</th>\n","      <td>1552</td>\n","    </tr>\n","    <tr>\n","      <th>Oregon</th>\n","      <td>1281</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":13}]},{"cell_type":"markdown","id":"b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d","metadata":{"id":"b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d"},"source":["4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions."]},{"cell_type":"code","source":["#Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions.\n","df_filtered = df.groupby([\"education\", \"gender\"])[\"customer_lifetime_value\"].agg([\"max\", \"min\", \"median\"])\n","df_filtered"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"WPUTVyf6fM-g","executionInfo":{"status":"ok","timestamp":1760911234551,"user_tz":-120,"elapsed":58,"user":{"displayName":"Rahala Moindze","userId":"02611134585983790642"}},"outputId":"4ef96c83-ae25-4f73-c619-5cacd3af7815"},"id":"WPUTVyf6fM-g","execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                     max          min       median\n","education            gender                                       \n","Bachelor             F       58753.88046  1904.000852  5752.485185\n","                     M       67907.27050  2030.783687  5797.377350\n","College              F       61850.18803  2004.350666  5642.596077\n","                     M       44795.46942  1918.119700  6005.847375\n","Doctor               F       44856.11397  2395.570000  5789.850900\n","                     M       32677.34284  2267.604038  5843.989737\n","High School or Below F       55277.44589  2150.178588  5978.754765\n","                     M       83325.38119  2132.771675  6081.481291\n","Master               F       51016.06704  2417.777032  5714.165863\n","                     M       50568.25912  2357.503128  5512.342258"],"text/html":["\n","  <div id=\"df-72f83628-fe7d-4794-93da-6db8881c630a\" class=\"colab-df-container\">\n","    <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></th>\n","      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The marketing team wants to analyze the number of policies sold by state and month. Present the data in a table where the months are arranged as columns and the states are arranged as rows."]},{"cell_type":"code","source":["#The marketing team wants to analyze the number of policies sold by state and month. Present the data in a table where the months are arranged as columns and the states are arranged as rows.\n","df_filtered = df.groupby([\"state\", \"effective_to_date\"])[\"number_of_policies\"].sum()\n","df_filtered.unstack()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":287},"id":"_4n-czXFfhFj","executionInfo":{"status":"ok","timestamp":1760911396914,"user_tz":-120,"elapsed":43,"user":{"displayName":"Rahala Moindze","userId":"02611134585983790642"}},"outputId":"5972fbc3-83d0-49d8-9610-a47d29b00148"},"id":"_4n-czXFfhFj","execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["effective_to_date  1/1/11  1/10/11  1/11/11  1/12/11  1/13/11  1/14/11  \\\n","state                                                                    \n","Arizona                55       58       32       49       62       31   \n","California             75      111      121       64       92       64   \n","Nevada                 29       10       18       22       12       15   \n","Oregon                 71      127       35       38       56       53   \n","Washington             18       30       14       15       16       34   \n","\n","effective_to_date  1/15/11  1/16/11  1/17/11  1/18/11  ...  2/26/11  2/27/11  \\\n","state                                                  ...                     \n","Arizona                 45       25       50       70  ...       39       33   \n","California              66       79       58      117  ...      101       51   \n","Nevada                  23        8       12       23  ...       21       26   \n","Oregon                  47       81       97       77  ...       57       45   \n","Washington               8       15       23       16  ...       13       23   \n","\n","effective_to_date  2/28/11  2/3/11  2/4/11  2/5/11  2/6/11  2/7/11  2/8/11  \\\n","state                                                                        \n","Arizona                 40      79      63      71      38      27      51   \n","California              57      86      70     104      71     100      33   \n","Nevada                  41      34      18      14       7      24      42   \n","Oregon                  58      82      76      80      67      94      47   \n","Washington              33      21      21      31      23      27       9   \n","\n","effective_to_date  2/9/11  \n","state                      \n","Arizona                30  \n","California             68  \n","Nevada                  8  \n","Oregon                 40  \n","Washington             14  \n","\n","[5 rows x 59 columns]"],"text/html":["\n","  <div id=\"df-398fce1c-402c-4470-ab8e-55df0466a5d8\" class=\"colab-df-container\">\n","    <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","    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             5154.764074       No   \n","13             13  KR82385  California              5454.587929       No   \n","16             16  FH51383  California              5326.677654       No   \n","...           ...      ...         ...                      ...      ...   \n","10902       10902  PP30874  California              3579.023825       No   \n","10903       10903  SU71163     Arizona              2771.663013       No   \n","10904       10904  QI63521      Nevada             19228.463620       No   \n","10906       10906  KX53892      Oregon              5259.444853       No   \n","10908       10908  WA60547  California             11971.977650       No   \n","\n","       coverage             education effective_to_date employmentstatus  \\\n","2         Basic              Bachelor           2/10/11         Employed   \n","3      Extended               College           1/11/11         Employed   \n","10     Extended  High School or Below            1/2/11         Employed   \n","13        Basic                Master           1/26/11         Employed   \n","16        Basic  High School or Below            2/7/11         Employed   \n","...         ...                   ...               ...              ...   \n","10902  Extended  High School or Below           1/24/11         Employed   \n","10903     Basic               College            1/7/11         Employed   \n","10904     Basic  High School or Below           2/24/11       Unemployed   \n","10906     Basic               College            1/6/11         Employed   \n","10908   Premium               College           2/13/11         Employed   \n","\n","      gender  ...  number_of_open_complaints number_of_policies  \\\n","2          M  ...                        0.0                  2   \n","3          M  ...                        0.0                  2   \n","10         M  ...                        0.0                  1   \n","13         M  ...                        0.0                  4   \n","16         F  ...                        0.0                  6   \n","...      ...  ...                        ...                ...   \n","10902      F  ...                        2.0                  1   \n","10903      M  ...                        4.0                  1   \n","10904      M  ...                        0.0                  2   \n","10906      F  ...                        0.0                  6   \n","10908      F  ...                        4.0                  6   \n","\n","          policy_type        policy  renew_offer_type  sales_channel  \\\n","2       Personal Auto   Personal L3            Offer3    Call Center   \n","3      Corporate Auto  Corporate L3            Offer2         Branch   \n","10     Corporate Auto  Corporate L3            Offer2         Branch   \n","13      Personal Auto   Personal L3            Offer4    Call Center   \n","16      Personal Auto   Personal L3            Offer4    Call Center   \n","...               ...           ...               ...            ...   \n","10902   Personal Auto   Personal L2            Offer2          Agent   \n","10903   Personal Auto   Personal L2            Offer2         Branch   \n","10904   Personal Auto   Personal L2            Offer1         Branch   \n","10906   Personal Auto   Personal L3            Offer2         Branch   \n","10908   Personal Auto   Personal L1            Offer1         Branch   \n","\n","       total_claim_amount  vehicle_class vehicle_size vehicle_type  \n","2              480.000000            SUV      Medsize            A  \n","3              484.013411  Four-Door Car      Medsize            A  \n","10             442.521087            SUV        Large            A  \n","13             331.200000   Two-Door Car      Medsize            A  \n","16             300.528579   Two-Door Car        Large            A  \n","...                   ...            ...          ...          ...  \n","10902          655.200000  Four-Door Car      Medsize            A  \n","10903          355.200000   Two-Door Car      Medsize            A  \n","10904          897.600000     Luxury SUV      Medsize            A  \n","10906          273.018929  Four-Door Car      Medsize            A  \n","10908          618.288849            SUV      Medsize            A  \n","\n","[4543 rows x 26 columns]"],"text/html":["\n","  <div id=\"df-8f272e3c-b3a8-4e9c-80c4-c9fcea09284c\" class=\"colab-df-container\">\n","    <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","      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Afterwards, you will need to sort the data by the count of policies sold in descending order.*\n","- *Next, you will select the top 3 states with the highest number of policies sold.*\n","- *Finally, you will create a new DataFrame that contains the number of policies sold by month for each of the top 3 states.*"]},{"cell_type":"code","source":["#Display a new DataFrame that contains the number of policies sold by month, by state, for the top 3 states with the highest number of policies sold.\n","\n","# Group by state and sum the number of policies to find the total for each state\n","state_policy_counts = df.groupby(\"state\")[\"number_of_policies\"].sum()\n","\n","# Sort the states by the total number of policies in descending order and select the top 3\n","top_3_states = state_policy_counts.sort_values(ascending=False).head(3).index\n","\n","# Filter the original DataFrame to include only the top 3 states\n","df_top_states = df[df[\"state\"].isin(top_3_states)]\n","\n","# Group the filtered data by state and month, sum the policies, and unstack to get the desired format\n","df_policies_by_month_state = df_top_states.groupby([\"state\", df_top_states[\"effective_to_date\"].str.split('/').str[0]])[\"number_of_policies\"].sum().unstack()\n","\n","df_policies_by_month_state"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"xbCYaw-4f82Z","executionInfo":{"status":"ok","timestamp":1760911572424,"user_tz":-120,"elapsed":51,"user":{"displayName":"Rahala Moindze","userId":"02611134585983790642"}},"outputId":"b3f5490d-12ae-425d-aa71-6fe57577ea72"},"id":"xbCYaw-4f82Z","execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["effective_to_date     1     2\n","state                        \n","Arizona            1344  1297\n","California         2439  2158\n","Oregon             2009  1885"],"text/html":["\n","  <div id=\"df-0917e6a5-2807-493a-b9ab-9d7ca1505a3d\" class=\"colab-df-container\">\n","    <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>effective_to_date</th>\n","      <th>1</th>\n","      <th>2</th>\n","    </tr>\n","    <tr>\n","      <th>state</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Arizona</th>\n","      <td>1344</td>\n","      <td>1297</td>\n","    </tr>\n","    <tr>\n","      <th>California</th>\n","      <td>2439</td>\n","      <td>2158</td>\n","    </tr>\n","    <tr>\n","      <th>Oregon</th>\n","      <td>2009</td>\n","      <td>1885</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0917e6a5-2807-493a-b9ab-9d7ca1505a3d')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-0917e6a5-2807-493a-b9ab-9d7ca1505a3d button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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The marketing team wants to analyze the effect of different marketing channels on the customer response rate.\n","\n","Hint: You can use melt to unpivot the data and create a table that shows the customer response rate (those who responded \"Yes\") by marketing channel."]},{"cell_type":"markdown","id":"e4378d94-48fb-4850-a802-b1bc8f427b2d","metadata":{"id":"e4378d94-48fb-4850-a802-b1bc8f427b2d"},"source":["External Resources for Data Filtering: https://towardsdatascience.com/filtering-data-frames-in-pandas-b570b1f834b9"]},{"cell_type":"code","execution_count":21,"id":"449513f4-0459-46a0-a18d-9398d974c9ad","metadata":{"id":"449513f4-0459-46a0-a18d-9398d974c9ad","colab":{"base_uri":"https://localhost:8080/","height":241},"executionInfo":{"status":"ok","timestamp":1760911665530,"user_tz":-120,"elapsed":60,"user":{"displayName":"Rahala Moindze","userId":"02611134585983790642"}},"outputId":"ee71dc5a-e2e2-4d16-a46d-2b463b405372"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["sales_channel\n","Agent          332\n","Branch         123\n","Call Center     97\n","Web             89\n","Name: response, dtype: int64"],"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>response</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>332</td>\n","    </tr>\n","    <tr>\n","      <th>Branch</th>\n","      <td>123</td>\n","    </tr>\n","    <tr>\n","      <th>Call Center</th>\n","      <td>97</td>\n","    </tr>\n","    <tr>\n","      <th>Web</th>\n","      <td>89</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":21}],"source":["# The marketing team wants to analyze the effect of different marketing channels on the customer response rate. You can use melt to unpivot the data and create a table that shows the customer response rate (those who responded \"Yes\") by marketing channel.\n","df_filtered = df[df[\"response\"] == \"Yes\"]\n","df_filtered = df_filtered.groupby([\"sales_channel\"])[\"response\"].count()\n","df_filtered"]}],"metadata":{"colab":{"provenance":[{"file_id":"1Zh_-RoDo-Y0W9BYFP8newGqJMRMhmtrE","timestamp":1760911673849}]},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.13"}},"nbformat":4,"nbformat_minor":5}
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