Python Question 1: Identifying High-Value Customers Using Python
An automotive retailer like Sonic Automotive aims to refine its customer engagement strategy by prioritizing high-value customers, or “power users.” These are customers who demonstrate significant purchasing activity. Define a Python function to identify these power users from a dataset of customer purchases. A “power user” is defined as a customer making more than 40 purchases in the last year.
Example Input:
purchases_data = [
{'purchase_id': 101, 'customer_id': 456, 'purchase_date': '01/08/2023', 'vehicle_id': 768, 'sales_value': 20000},
{'purchase_id': 202, 'customer_id': 789, 'purchase_date': '04/05/2023', 'vehicle_id': 967, 'sales_value': 15000},
{'purchase_id': 303, 'customer_id': 123, 'purchase_date': '10/02/2023', 'vehicle_id': 564, 'sales_value': 35000},
{'purchase_id': 404, 'customer_id': 654, 'purchase_date': '04/26/2023', 'vehicle_id': 432, 'sales_value': 22000},
{'purchase_id': 505, 'customer_id': 789, 'purchase_date': '05/01/2023', 'vehicle_id': 860, 'sales_value': 19000},
]Example Output:
power_users = {789: 2, 456: 1, 123: 1, 654: 1} # Customer_id: total_purchasesAnswer:
This Python function processes a list of purchase dictionaries to identify customers who have made more than 40 purchases in the last year. By pinpointing these power users, automotive companies can tailor marketing efforts and improve customer loyalty programs.
from datetime import datetime, timedelta
def identify_power_users(purchases, purchase_threshold=40):
"""
Identifies power users from a list of purchase records.
Args:
purchases (list): List of dictionaries, each representing a purchase.
purchase_threshold (int): Minimum number of purchases to be considered a power user.
Returns:
dict: Dictionary of power users with customer_id as keys and total purchases as values.
"""
power_users = {}
one_year_ago = datetime.now() - timedelta(days=365)
for purchase in purchases:
purchase_date = datetime.strptime(purchase['purchase_date'], '%d/%m/%Y')
if purchase_date >= one_year_ago: # Consider only purchases within the last year
customer_id = purchase['customer_id']
power_users[customer_id] = power_users.get(customer_id, 0) + 1
return {k: v for k, v in power_users.items() if v > purchase_threshold}
Example usage with the input data:
power_users_result = identify_power_users(purchases_data)
print(power_users_result)
Python Question 2: Employee Compensation Analysis using Pandas
Given employee salary data for Sonic Automotive, use Python with Pandas to find all employees who earn more than their managers. This involves data manipulation and comparison within a structured dataset.
Sonic Automotive Example Input (CSV format assumed for Pandas):
employee_id,name,salary,department_id,manager_id
1,Emma Thompson,3800,1,
2,Daniel Rodriguez,2230,1,10
3,Olivia Smith,8000,1,8
4,Noah Johnson,6800,2,8
5,Sophia Martinez,1750,1,10
8,William Davis,7000,2,
10,James Anderson,4000,1,Example Output:
Output should be a Pandas DataFrame or similar structure
employee_id employee_name
0 3 Olivia SmithThis output indicates Olivia Smith earns $8,000, exceeding her manager William Davis’s salary of $7,000.
Answer:
This Python solution utilizes Pandas to efficiently process and compare employee and manager salaries. It demonstrates merging dataframes and applying conditional filtering to identify employees out-earning their managers, a common task in HR and compensation analysis within automotive companies.
import pandas as pd
import io
csv_data = """employee_id,name,salary,department_id,manager_id
1,Emma Thompson,3800,1,
2,Daniel Rodriguez,2230,1,10
3,Olivia Smith,8000,1,8
4,Noah Johnson,6800,2,8
5,Sophia Martinez,1750,1,10
8,William Davis,7000,2,
10,James Anderson,4000,1,"""
df_employees = pd.read_csv(io.StringIO(csv_data))
Fill NaN manager_id with 0 for easier handling in merge (assuming no employee has manager_id 0)
df_employees['manager_id'] = df_employees['manager_id'].fillna(0).astype(int)
Create a managers dataframe
df_managers = df_employees[['employee_id', 'salary']].rename(columns={'employee_id': 'manager_id', 'salary': 'manager_salary'})
Merge employees and managers dataframes
merged_df = pd.merge(df_employees, df_managers, on='manager_id', suffixes=('_emp', '_mgr'), how='left')
Filter employees who earn more than their managers, excluding those without managers (manager_id = 0 after fillna)
richer_employees = merged_df[(merged_df['salary_emp'] > merged_df['manager_salary']) & (merged_df['manager_id'] != 0)][['employee_id', 'name']]
richer_employees.columns = ['employee_id', 'employee_name']
print(richer_employees)Python Question 3: Simulating Vehicle Telemetry Data
In the age of connected cars, telemetry data is vital. Write a Python script to simulate real-time telemetry data from a fleet of vehicles. Include parameters like speed, engine temperature, and GPS coordinates.
This Python code snippet generates simulated telemetry data, crucial for testing and developing applications for connected vehicles in the automotive industry.
import time
import random
import json
def simulate_telemetry():
"""Simulates vehicle telemetry data."""
vehicle_id = "Vehicle-" + str(random.randint(1000, 9999))
speed = random.randint(0, 120) # km/h
engine_temp = random.randint(80, 110) # Celsius
lat = 34.0522 + random.uniform(-0.1, 0.1) # Example latitude range
lon = -118.2437 + random.uniform(-0.1, 0.1) # Example longitude range
telemetry_data = {
"vehicle_id": vehicle_id,
"timestamp": time.time(),
"speed": speed,
"engine_temp": engine_temp,
"gps": {"latitude": lat, "longitude": lon}
}
return telemetry_data
if __name__ == "__main__":
while True:
data = simulate_telemetry()
print(json.dumps(data)) # Output as JSON for easy parsing
time.sleep(1) # Simulate data every secondPython Question 4: Analyzing Car Service History
Imagine you are a data analyst at Sonic Automotive tasked with analyzing car service records to determine the last service date and type for each vehicle. Given a dataset of car service records, write a Python function using Pandas to find the latest service for each car.
Example Input:
service_records = [
{'service_id': 101, 'car_id': 2001, 'service_type': 'Oil Change', 'service_date': '2022-05-14'},
{'service_id': 102, 'car_id': 2001, 'service_type': 'Tyre Change', 'service_date': '2022-06-01'},
{'service_id': 103, 'car_id': 2002, 'service_type': 'Engine Check', 'service_date': '2022-07-10'},
{'service_id': 104, 'car_id': 2003, 'service_type': 'Tyre Change', 'service_date': '2022-01-18'},
{'service_id': 105, 'car_id': 2003, 'service_type': 'Oil Change', 'service_date': '2022-06-30'}
]Example Output:
Output should be a Pandas DataFrame or similar structure
car_id latest_service_date service_type
0 2001 2022-06-01 Tyre Change
1 2002 2022-07-10 Engine Check
2 2003 2022-06-30 Oil ChangeAnswer:
This Python solution leverages Pandas to process service records and efficiently determine the latest service for each car. It showcases skills in data manipulation and date handling, essential for automotive data analysis related to vehicle maintenance and customer service.
import pandas as pd
service_data = [
{'service_id': 101, 'car_id': 2001, 'service_type': 'Oil Change', 'service_date': '2022-05-14'},
{'service_id': 102, 'car_id': 2001, 'service_type': 'Tyre Change', 'service_date': '2022-06-01'},
{'service_id': 103, 'car_id': 2002, 'service_type': 'Engine Check', 'service_date': '2022-07-10'},
{'service_id': 104, 'car_id': 2003, 'service_type': 'Tyre Change', 'service_date': '2022-01-18'},
{'service_id': 105, 'car_id': 2003, 'service_type': 'Oil Change', 'service_date': '2022-06-30'}
]
df_services = pd.DataFrame(service_data)
df_services['service_date'] = pd.to_datetime(df_services['service_date'])
Find the latest service date for each car
latest_services = df_services.loc[df_services.groupby('car_id')['service_date'].idxmax()]
latest_services = latest_services[['car_id', 'service_date', 'service_type']].rename(columns={'service_date': 'latest_service_date'})
latest_services = latest_services.sort_values(by='car_id').reset_index(drop=True)
print(latest_services)Python Question 5: Handling Missing Customer Engagement Data
The COALESCE() function in SQL is analogous to handling missing values in Python. Suppose you are analyzing customer engagement data for Sonic Automotive. You have data on email and SMS engagement, but some values are missing (represented as None in Python). Use Python to fill in these missing values with default engagement levels.
Example Input:
engagement_data = [
{'customer_id': 101, 'email_engagement': 'moderately_active', 'sms_engagement': 'not_opted_in'},
{'customer_id': 201, 'email_engagement': 'un-subscribed', 'sms_engagement': None},
{'customer_id': 301, 'email_engagement': None, 'sms_engagement': 'not_opted_in'},
{'customer_id': 401, 'email_engagement': 'not_active', 'sms_engagement': 'very_active'},
{'customer_id': 501, 'email_engagement': 'very_active', 'sms_engagement': 'very_active'},
{'customer_id': 601, 'email_engagement': None, 'sms_engagement': None}
]Expected Output:
updated_engagement_data = [
{'customer_id': 101, 'email_engagement': 'moderately_active', 'sms_engagement': 'not_opted_in'},
{'customer_id': 201, 'email_engagement': 'un-subscribed', 'sms_engagement': 'not_opted_in'},
{'customer_id': 301, 'email_engagement': 'not_active', 'sms_engagement': 'not_opted_in'},
{'customer_id': 401, 'email_engagement': 'not_active', 'sms_engagement': 'very_active'},
{'customer_id': 501, 'email_engagement': 'very_active', 'sms_engagement': 'very_active'},
{'customer_id': 601, 'email_engagement': 'not_active', 'sms_engagement': 'not_opted_in'}
]Answer:
This Python code demonstrates how to handle missing data, a crucial skill in data analysis. It replaces None values with default engagement levels, mimicking the functionality of SQL’s COALESCE() and ensuring data completeness for further analysis in automotive customer engagement strategies.
engagement_data = [
{'customer_id': 101, 'email_engagement': 'moderately_active', 'sms_engagement': 'not_opted_in'},
{'customer_id': 201, 'email_engagement': 'un-subscribed', 'sms_engagement': None},
{'customer_id': 301, 'email_engagement': None, 'sms_engagement': 'not_opted_in'},
{'customer_id': 401, 'email_engagement': 'not_active', 'sms_engagement': 'very_active'},
{'customer_id': 501, 'email_engagement': 'very_active', 'sms_engagement': 'very_active'},
{'customer_id': 601, 'email_engagement': None, 'sms_engagement': None}
]
default_email_engagement = 'not_active'
default_sms_engagement = 'not_opted_in'
updated_engagement_data = []
for record in engagement_data:
updated_record = record.copy()
if updated_record['email_engagement'] is None:
updated_record['email_engagement'] = default_email_engagement
if updated_record['sms_engagement'] is None:
updated_record['sms_engagement'] = default_sms_engagement
updated_engagement_data.append(updated_record)
print(updated_engagement_data)Python Question 6: Filtering Customer Records Based on Multiple Criteria
Given a customer records dataset for Sonic Automotive, write a Python function to filter customers who purchased a car in 2020, reside in California, and bought either a ‘Sedan’ or ‘SUV’.
Example Input:
customer_records = [
{'customerID': 1201, 'purchaseDate': '2019-03-21', 'state': 'California', 'carType': 'Sedan'},
{'customerID': 2342, 'purchaseDate': '2020-05-17', 'state': 'California', 'carType': 'SUV'},
{'customerID': 9812, 'purchaseDate': '2020-08-30', 'state': 'New York', 'carType': 'SUV'},
{'customerID': 3452, 'purchaseDate': '2020-12-15', 'state': 'California', 'carType': 'Sedan'},
{'customerID': 2651, 'purchaseDate': '2018-07-07', 'state': 'California', 'carType': 'Pickup'}
]Example Output:
filtered_customers = [
{'customerID': 2342, 'purchaseDate': '2020-05-17', 'state': 'California', 'carType': 'SUV'},
{'customerID': 3452, 'purchaseDate': '2020-12-15', 'state': 'California', 'carType': 'Sedan'}
]Answer:
This Python function demonstrates filtering data based on multiple conditions, a common task in data analysis for automotive sales and marketing. It uses conditional logic to select customer records that meet specific criteria related to purchase year, location, and vehicle type.
customer_records = [
{'customerID': 1201, 'purchaseDate': '2019-03-21', 'state': 'California', 'carType': 'Sedan'},
{'customerID': 2342, 'purchaseDate': '2020-05-17', 'state': 'California', 'carType': 'SUV'},
{'customerID': 9812, 'purchaseDate': '2020-08-30', 'state': 'New York', 'carType': 'SUV'},
{'customerID': 3452, 'purchaseDate': '2020-12-15', 'state': 'California', 'carType': 'Sedan'},
{'customerID': 2651, 'purchaseDate': '2018-07-07', 'state': 'California', 'carType': 'Pickup'}
]
def filter_customers(records):
"""
Filters customer records based on purchase year, state, and car type.
Args:
records (list): List of customer record dictionaries.
Returns:
list: Filtered list of customer record dictionaries.
"""
filtered_customers = []
for customer in records:
purchase_year = customer['purchaseDate'][:4] # Extract year from purchaseDate string
if purchase_year == '2020' and customer['state'] == 'California' and customer['carType'] in ['Sedan', 'SUV']:
filtered_customers.append(customer)
return filtered_customers
filtered_customers_result = filter_customers(customer_records)
print(filtered_customers_result)Python Question 7: Finding Common Leads Across CRM Systems
The concept of INTERSECT in SQL is about identifying common records. Imagine Sonic Automotive uses both Salesforce and Hubspot CRM systems. You need to find leads created before 2023 that are present in both CRMs. How would you achieve this using Python sets?
This Python code demonstrates set intersection to find common elements, analogous to SQL’s INTERSECT, useful for data reconciliation across different systems in automotive sales and marketing.
Python Question 8: Calculating Average Vehicle Selling Price by Make
Sonic Automotive is interested in the average selling price of vehicles by make in 2022. Given sales and vehicle data, write a Python function using Pandas to calculate the average selling price for each vehicle make in 2022.
Sample Input (CSV format assumed for Pandas):
sales_data.csv
sale_id,vehicle_id,sale_date,sale_price
1,123,01/07/2022 00:00:00,30200
2,265,01/14/2022 00:00:00,40750
3,362,01/21/2022 00:00:00,35990
4,192,01/28/2022 00:00:00,27450
5,981,02/04/2022 00:00:00,38500vehicles_data.csv
vehicle_id,make,model
123,Toyota,Camry
265,Honda,Accord
362,Honda,Civic
192,Toyota,Corolla
981,Ford,MustangExample Output:
Output should be a Pandas DataFrame or similar structure
make avg_sale_price
0 Ford 38500.0
1 Honda 38370.0
2 Toyota 28825.0Answer:
This Python solution uses Pandas to join sales and vehicle data, filter for sales in 2022, and calculate the average selling price by vehicle make. It showcases data aggregation and time-based filtering, essential for automotive sales performance analysis.
import pandas as pd
import io
sales_csv_data = """sale_id,vehicle_id,sale_date,sale_price
1,123,01/07/2022 00:00:00,30200
2,265,01/14/2022 00:00:00,40750
3,362,01/21/2022 00:00:00,35990
4,192,01/28/2022 00:00:00,27450
5,981,02/04/2022 00:00:00,38500"""
vehicles_csv_data = """vehicle_id,make,model
123,Toyota,Camry
265,Honda,Accord
362,Honda,Civic
192,Toyota,Corolla
981,Ford,Mustang"""
df_sales = pd.read_csv(io.StringIO(sales_csv_data))
df_vehicles = pd.read_csv(io.StringIO(vehicles_csv_data))
Convert sale_date to datetime and extract year
df_sales['sale_date'] = pd.to_datetime(df_sales['sale_date'])
df_sales['sale_year'] = df_sales['sale_date'].dt.year
Filter sales for the year 2022
sales_2022 = df_sales[df_sales['sale_year'] == 2022]
Merge sales data with vehicle data
merged_df = pd.merge(sales_2022, df_vehicles, on='vehicle_id')
Calculate average sale price by make
avg_prices = merged_df.groupby('make')['sale_price'].mean().reset_index()
avg_prices.columns = ['make', 'avg_sale_price']
avg_prices = avg_prices.sort_values(by='avg_sale_price', ascending=False).reset_index(drop=True)
print(avg_prices)Python Question 9: Calculating Click-Through Conversion Rate (CTR)
Calculate the Click-Through Conversion Rate (CTR) from vehicle product page views to purchases for Sonic Automotive. Use Python to process view and purchase data and determine the CTR for each vehicle.
Example Input:
view_data = [
{'view_id': 1347, 'user_id': 456, 'view_date': '2022-06-08', 'vehicle_id': 32001},
{'view_id': 1594, 'user_id': 789, 'view_date': '2022-06-10', 'vehicle_id': 41006},
{'view_id': 1893, 'user_id': 654, 'view_date': '2022-06-18', 'vehicle_id': 32001},
{'view_id': 2075, 'user_id': 321, 'view_date': '2022-07-26', 'vehicle_id': 41006},
{'view_id': 2310, 'user_id': 987, 'view_date': '2022-07-05', 'vehicle_id': 41006}
]
purchase_data = [
{'purchase_id': 9671, 'user_id': 456, 'purchase_date': '2022-06-10', 'vehicle_id': 32001},
{'purchase_id': 10078, 'user_id': 789, 'purchase_date': '2022-06-30', 'vehicle_id': 41006},
{'purchase_id': 10504, 'user_id': 654, 'purchase_date': '2022-07-18', 'vehicle_id': 32001},
{'purchase_id': 10962, 'user_id': 321, 'purchase_date': '2022-07-26', 'vehicle_id': 41006},
{'purchase_id': 11238, 'user_id': 987, 'purchase_date': '2022-07-05', 'vehicle_id': 41006}
]Answer:
This Python code calculates Click-Through Conversion Rate (CTR), a key metric in digital marketing and sales analytics for the automotive industry. It joins view and purchase data to correlate vehicle views with purchases and computes the conversion rate for each vehicle.
view_data = [
{'view_id': 1347, 'user_id': 456, 'view_date': '2022-06-08', 'vehicle_id': 32001},
{'view_id': 1594, 'user_id': 789, 'view_date': '2022-06-10', 'vehicle_id': 41006},
{'view_id': 1893, 'user_id': 654, 'view_date': '2022-06-18', 'vehicle_id': 32001},
{'view_id': 2075, 'user_id': 321, 'view_date': '2022-07-26', 'vehicle_id': 41006},
{'view_id': 2310, 'user_id': 987, 'view_date': '2022-07-05', 'vehicle_id': 41006}
]
purchase_data = [
{'purchase_id': 9671, 'user_id': 456, 'purchase_date': '2022-06-10', 'vehicle_id': 32001},
{'purchase_id': 10078, 'user_id': 789, 'purchase_date': '2022-06-30', 'vehicle_id': 41006},
{'purchase_id': 10504, 'user_id': 654, 'purchase_date': '2022-07-18', 'vehicle_id': 32001},
{'purchase_id': 10962, 'user_id': 321, 'purchase_date': '2022-07-26', 'vehicle_id': 41006},
{'purchase_id': 11238, 'user_id': 987, 'purchase_date': '2022-07-05', 'vehicle_id': 41006}
]
def calculate_ctr(views, purchases):
"""
Calculates Click-Through Conversion Rate (CTR) for each vehicle.
Args:
views (list): List of view dictionaries.
purchases (list): List of purchase dictionaries.
Returns:
dict: Dictionary of CTR for each vehicle_id.
"""
view_counts = {}
purchase_counts = {}
for view in views:
vehicle_id = view['vehicle_id']
user_id = view['user_id'] # Consider unique users for views
if vehicle_id not in view_counts:
view_counts[vehicle_id] = set()
view_counts[vehicle_id].add(user_id)
for purchase in purchases:
vehicle_id = purchase['vehicle_id']
user_id = purchase['user_id'] # Consider unique users for purchases
if vehicle_id not in purchase_counts:
purchase_counts[vehicle_id] = set()
purchase_counts[vehicle_id].add(user_id)
ctr_rates = {}
for vehicle_id in view_counts.keys():
views_unique_users = len(view_counts[vehicle_id])
purchases_unique_users = len(purchase_counts.get(vehicle_id, set())) # Default to 0 if no purchases
ctr = (purchases_unique_users / views_unique_users) * 100 if views_unique_users > 0 else 0
ctr_rates[vehicle_id] = ctr
return ctr_rates
ctr_results = calculate_ctr(view_data, purchase_data)
Format output for better readability
formatted_ctr_results = []
for vehicle_id, ctr in ctr_results.items():
formatted_ctr_results.append({'vehicle_id': vehicle_id, 'CTR': f"{ctr:.2f}%"})
print(formatted_ctr_results)Python Question 10: Identifying Records Not Present in Another Dataset
The LEFT JOIN ... WHERE IS NULL pattern in SQL is used to find records in one table that are not present in another. Using Python, how would you determine which employees are in the ’employees’ dataset but not in the ‘managers’ dataset?
This Python code effectively simulates the LEFT JOIN ... WHERE IS NULL operation in SQL, useful for identifying discrepancies or unique entries between datasets, such as employee lists and manager lists within automotive organizations.
Preparing for Python Coding Interviews in Automotive
Excelling in a Python coding interview for an automotive company involves consistent practice. Beyond these example questions, it’s beneficial to explore broader Python interview resources and tailor your preparation to the automotive domain.
To further prepare, consider practicing with diverse Python coding challenges and exploring datasets relevant to the automotive industry. Understanding the applications of Python in areas like vehicle data analysis, autonomous driving, and smart mobility will greatly enhance your interview readiness.
If you need to strengthen your Python fundamentals, numerous online tutorials and courses are available to build a solid foundation before tackling interview-specific questions.
Key Areas in Automotive Data Science Interviews
Besides Python coding skills, automotive data science interviews often assess:
- Data Analysis and Visualization
- Machine Learning for Predictive Modeling in Automotive
- Statistical Analysis and A/B Testing for Automotive Applications
- Understanding of Automotive Industry Metrics and KPIs
- Behavioral Questions related to problem-solving and teamwork in a technical context.
To comprehensively prepare for automotive data science interviews, explore resources like “Ace the Data Science Interview” for a broader range of interview questions and essential data science concepts.
