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169 changes: 164 additions & 5 deletions lab-dw-aggregating.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -127,14 +127,173 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"id": "449513f4-0459-46a0-a18d-9398d974c9ad",
"metadata": {
"id": "449513f4-0459-46a0-a18d-9398d974c9ad"
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1. Customers with low total claim amount and 'Yes' to response:\n",
" Unnamed: 0 Customer State Customer Lifetime Value Response \\\n",
"3 3 XL78013 Oregon 22332.439460 Yes \n",
"8 8 FM55990 California 5989.773931 Yes \n",
"15 15 CW49887 California 4626.801093 Yes \n",
"19 19 NJ54277 California 3746.751625 Yes \n",
"27 27 MQ68407 Oregon 4376.363592 Yes \n",
"\n",
" Coverage Education Effective To Date EmploymentStatus Gender ... \\\n",
"3 Extended College 1/11/11 Employed M ... \n",
"8 Premium College 1/19/11 Employed M ... \n",
"15 Basic Master 1/16/11 Employed F ... \n",
"19 Extended College 2/26/11 Employed F ... \n",
"27 Premium Bachelor 2/28/11 Employed F ... \n",
"\n",
" Number of Open Complaints Number of Policies Policy Type \\\n",
"3 0.0 2 Corporate Auto \n",
"8 0.0 1 Personal Auto \n",
"15 0.0 1 Special Auto \n",
"19 1.0 1 Personal Auto \n",
"27 0.0 1 Personal Auto \n",
"\n",
" Policy Renew Offer Type Sales Channel Total Claim Amount \\\n",
"3 Corporate L3 Offer2 Branch 484.013411 \n",
"8 Personal L1 Offer2 Branch 739.200000 \n",
"15 Special L1 Offer2 Branch 547.200000 \n",
"19 Personal L2 Offer2 Call Center 19.575683 \n",
"27 Personal L3 Offer2 Agent 60.036683 \n",
"\n",
" Vehicle Class Vehicle Size Vehicle Type \n",
"3 Four-Door Car Medsize A \n",
"8 Sports Car Medsize NaN \n",
"15 SUV Medsize NaN \n",
"19 Two-Door Car Large A \n",
"27 Four-Door Car Medsize NaN \n",
"\n",
"[5 rows x 26 columns]\n",
"\n",
"2. Average metrics by policy type and gender for 'Yes' responses:\n",
" Policy Type Gender Monthly Premium Auto Customer Lifetime Value \\\n",
"0 Corporate Auto F 94.301775 7712.628736 \n",
"1 Corporate Auto M 92.188312 7944.465414 \n",
"2 Personal Auto F 98.998148 8339.791842 \n",
"3 Personal Auto M 91.085821 7448.383281 \n",
"4 Special Auto F 92.314286 7691.584111 \n",
"5 Special Auto M 86.343750 8247.088702 \n",
"\n",
" Total Claim Amount \n",
"0 433.738499 \n",
"1 408.582459 \n",
"2 452.965929 \n",
"3 457.010178 \n",
"4 453.280164 \n",
"5 429.527942 \n",
"\n",
"3. States with more than 500 customers:\n",
" State Customer Count\n",
"0 California 3552\n",
"1 Oregon 2909\n",
"2 Arizona 1937\n",
"3 Nevada 993\n",
"4 Washington 888\n",
"\n",
"4. Customer lifetime value stats by education and gender:\n",
" max min median\n",
"Education Gender \n",
"Bachelor F 73225.95652 1904.000852 5640.505303\n",
" M 67907.27050 1898.007675 5548.031892\n",
"College F 61850.18803 1898.683686 5623.611187\n",
" M 61134.68307 1918.119700 6005.847375\n",
"Doctor F 44856.11397 2395.570000 5332.462694\n",
" M 32677.34284 2267.604038 5577.669457\n",
"High School or Below F 55277.44589 2144.921535 6039.553187\n",
" M 83325.38119 1940.981221 6286.731006\n",
"Master F 51016.06704 2417.777032 5729.855012\n",
" M 50568.25912 2272.307310 5579.099207\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\LAP-MPC\\AppData\\Local\\Temp\\ipykernel_11684\\4166574666.py:34: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" df['Effective To Date'] = pd.to_datetime(df['Effective To Date'])\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"5. Policies sold by state and month:\n",
"Month February January\n",
"State \n",
"Arizona 929 1008\n",
"California 1634 1918\n",
"Nevada 442 551\n",
"Oregon 1344 1565\n",
"Washington 425 463\n",
"\n",
"6. Number of policies sold by month for the top 3 states:\n",
" State Month Policy Count\n",
"0 Arizona February 929\n",
"1 Arizona January 1008\n",
"2 California February 1634\n",
"3 California January 1918\n",
"6 Oregon February 1344\n",
"7 Oregon January 1565\n"
]
}
],
"source": [
"# your code goes here"
"import pandas as pd\n",
"\n",
"# Load the dataset\n",
"url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\"\n",
"df = pd.read_csv(url)\n",
"\n",
"# 1. Filter for low total claim amount and 'Yes' responses\n",
"low_claim_yes_response_df = df[(df['Total Claim Amount'] < 1000) & (df['Response'] == 'Yes')]\n",
"print(\"1. Customers with low total claim amount and 'Yes' to response:\")\n",
"print(low_claim_yes_response_df.head())\n",
"\n",
"# 2. Average metrics by policy type and gender for 'Yes' responses\n",
"avg_metrics_df = df[df['Response'] == 'Yes'].groupby(['Policy Type', 'Gender']).agg({\n",
" 'Monthly Premium Auto': 'mean',\n",
" 'Customer Lifetime Value': 'mean',\n",
" 'Total Claim Amount': 'mean'\n",
"}).reset_index()\n",
"print(\"\\n2. Average metrics by policy type and gender for 'Yes' responses:\")\n",
"print(avg_metrics_df)\n",
"\n",
"# 3. Count customers per state (only where more than 500 customers)\n",
"customers_by_state = df['State'].value_counts().reset_index()\n",
"customers_by_state.columns = ['State', 'Customer Count']\n",
"filtered_states = customers_by_state[customers_by_state['Customer Count'] > 500]\n",
"print(\"\\n3. States with more than 500 customers:\")\n",
"print(filtered_states)\n",
"\n",
"# 4. Lifetime value stats by education and gender\n",
"clv_stats = df.groupby(['Education', 'Gender'])['Customer Lifetime Value'].agg(['max', 'min', 'median'])\n",
"print(\"\\n4. Customer lifetime value stats by education and gender:\")\n",
"print(clv_stats)\n",
"\n",
"# 5. Policies sold by state and month\n",
"df['Effective To Date'] = pd.to_datetime(df['Effective To Date'])\n",
"df['Month'] = df['Effective To Date'].dt.strftime('%B')\n",
"policies_by_state_month = df.groupby(['State', 'Month']).size().unstack(fill_value=0)\n",
"print(\"\\n5. Policies sold by state and month:\")\n",
"print(policies_by_state_month)\n",
"\n",
"# 6. Find the top 3 states with the most policies sold\n",
"policies_by_state_month_count = df.groupby(['State', 'Month']).size().reset_index(name='Policy Count')\n",
"top_states_by_policy = policies_by_state_month_count.groupby('State')['Policy Count'].sum().nlargest(3).index\n",
"top_policies_by_state_month = policies_by_state_month_count[policies_by_state_month_count['State'].isin(top_states_by_policy)]\n",
"print(\"\\n6. Number of policies sold by month for the top 3 states:\")\n",
"print(top_policies_by_state_month)"
]
}
],
Expand All @@ -143,7 +302,7 @@
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -157,7 +316,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
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"nbformat": 4,
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