Pro90 ProblemsSQL + Python
Lodging SQL & Python Interview Questions
Booking platforms analyze reservation data, host performance, and pricing dynamics across global markets. These SQL and Python challenges are modeled after work at Airbnb, Booking.com, Expedia, Marriott, Hilton, Hyatt, VRBO, Wyndham, Tripadvisor, IHG, and more. Practice occupancy rate analysis, RevPAR calculation, pricing optimization, booking lead time trends, and host quality metrics.
Companies working in Lodging
These practice problems are modeled after the kind of data and analytics challenges teams in this industry typically face.
Company names and logos are trademarks of their respective owners, used here only to describe the kind of data these companies work with. Let's Data Science is not affiliated with, endorsed by, or sponsored by any company shown. Practice problems are original works and are not real interview questions from these companies. Rights & takedowns.
Difficulty Distribution
Easy
15
17% of problems
Medium
32
36% of problems
Hard
38
42% of problems
Expert
5
6% of problems
What You'll Practice
Occupancy rate analysis
RevPAR calculations
Booking funnel metrics
Host performance scoring
Pricing optimization
Seasonal demand patterns
Guest satisfaction analysis
Market penetration
Topics Covered
SQL· 9
aggregationbasic queries filteringcleaning transformdate timejoinsscenario sqlset operationssubqueries cteswindow functions
Python· 12
eda statisticsfeature engineeringpandas aggregationpandas applypandas basicspandas cleaningpandas datetimepandas filteringpandas mergingpandas reshapingpandas scenariopandas window
All Problems90 total
01
Active Five-Star HotelsPro
SQLEasy02Refundable Rate PlansPro
SQLEasy03Ocean View King Bed RoomsPro
SQLEasy04High-Value Direct BookingsPro
SQLMedium05Platinum Guests With EmailPro
SQLEasy06Reservations With Property DetailsPro
SQLEasy07Bookings With Guest and Room InfoPro
SQLMedium08Reservations With Cancellation PolicyPro
SQLMedium09Guest Reviews With Property ContextPro
SQLMedium10Room Types Never Booked DirectlyPro
SQLHard11Guests Who Never Left a ReviewPro
SQLMedium12Booking Volume by ChannelPro
SQLEasy13Total Revenue per PropertyPro
SQLMedium14Average Nightly Rate by Room TypePro
SQLMedium15Cancellation Rate by Booking ChannelPro
SQLMedium16Top 5 Guests by Lifetime SpendPro
SQLHard17Properties With High No-Show RatePro
SQLHard18Rank Properties by Total RevenuePro
SQLMedium19Guest Booking Sequence NumberPro
SQLMedium20Daily Revenue Running Total per PropertyPro
SQLHard21Most Expensive Booking per PropertyPro
SQLHard22Property Rating With 3-Review Moving AveragePro
SQLHard23Revenue Change From Prior BookingPro
SQLHard24Room Occupancy Quartile AnalysisPro
SQLHard25Properties Above Average RevenuePro
SQLMedium26Guests Booking Direct and OTAPro
SQLHard27Latest Booking per GuestPro
SQLHard28Revenue Summary With Rank via CTEPro
SQLHard29Channels With Above-Average ADRPro
SQLHard30Recent Bookings in August 2024Pro
SQLEasy31Monthly Booking Volume TrendPro
SQLMedium32Average Booking Lead Time by ChannelPro
SQLMedium33Late Cancellations Within Free PeriodPro
SQLHard34Bookings With Stay Length CategoryPro
SQLEasy35Revenue With Commission Tier LabelPro
SQLMedium36Guest Loyalty Status NormalizedPro
SQLHard37Properties With Bookings or ReviewsPro
SQLMedium38Guests Who Booked But Never ReviewedPro
SQLMedium39Property Revenue ScorecardPro
SQLHard40Channel Performance ReportPro
SQLHard41Guest Lifetime Value SummaryPro
SQLHard42Cancellation and No-Show Financial ImpactPro
SQLHard43Payment Reconciliation AuditPro
SQLHard44Property Operations DashboardPro
SQLExpert45OTA Commission vs Direct Booking AnalysisPro
SQLHard46Active Property ProfilesPro
PYTHONEasy47Room Type Bed DistributionPro
PYTHONEasy48Guest Loyalty Tier SummaryPro
PYTHONEasy49Rate Plan Overview by MealPro
PYTHONMedium50Active Direct-Channel ReservationsPro
PYTHONEasy51High-Value Checked-Out BookingsPro
PYTHONMedium52Refundable Rate Plans With MealsPro
PYTHONMedium53Premium Guest Luxury StaysPro
PYTHONHard54Reservations Count by ChannelPro
PYTHONEasy55Total Revenue by PropertyPro
PYTHONMedium56Average Review Rating by PlatformPro
PYTHONMedium57Property Booking MetricsPro
PYTHONHard58Channel Commission AnalysisPro
PYTHONHard59Reservations with Guest NamesPro
PYTHONEasy60Payments with Booking ChannelPro
PYTHONMedium61Room Types Without ReservationsPro
PYTHONMedium62Review Details with PropertyPro
PYTHONHard63Full Reservation HierarchyPro
PYTHONHard64Rank Properties by RevenuePro
PYTHONMedium65Running Total Revenue per PropertyPro
PYTHONMedium66Seven-Day Moving Average OccupancyPro
PYTHONHard67Daily Revenue Change vs Previous DayPro
PYTHONHard68Reservation Booking Month and YearPro
PYTHONEasy69Booking Lead Time CalculationPro
PYTHONMedium70Monthly Booking Volume by ChannelPro
PYTHONHard71Fill Missing Guest ContactsPro
PYTHONMedium72Normalize Payment AmountsPro
PYTHONMedium73Standardize Booking ChannelsPro
PYTHONHard74Reservation Count by Channel and StatusPro
PYTHONMedium75Property Revenue by Booking ChannelPro
PYTHONHard76Classify Reservations by Value TierPro
PYTHONMedium77Guest Value ClassificationPro
PYTHONHard78Compute Occupancy RatePro
PYTHONMedium79Reservation Value Quartile BucketingPro
PYTHONHard80Time-Based Booking FeaturesPro
PYTHONHard81Property Performance MatrixPro
PYTHONExpert82Review Text FeaturesPro
PYTHONHard83Nightly Rate Descriptive StatsPro
PYTHONMedium84Rating vs Revenue CorrelationPro
PYTHONHard85Anomalous Booking Amount DetectionPro
PYTHONHard86Property Performance ScorecardPro
PYTHONHard87Guest Lifetime Value ReportPro
PYTHONHard88Full-Funnel Booking ReportPro
PYTHONExpert89Channel Profitability ClassificationPro
PYTHONExpert90Housekeeping Efficiency AnalysisPro
PYTHONExpertReady to practice Lodging?
90 SQL and Python challenges built from real lodging data. Graded instantly in your browser — no setup required.