Pro90 ProblemsSQL + Python
Ride-Hailing SQL & Python Interview Questions
Mobility platforms process millions of trips daily, requiring analysis of surge pricing, driver utilization, and geographic demand patterns. These SQL and Python challenges are modeled after work at Uber, Lyft, DiDi, Grab, Ola, Waymo, Bolt, Lime, Via, Cabify, and more. Build skills in trip matching efficiency, driver earnings optimization, surge pricing models, geographic demand forecasting, and safety analytics.
Companies working in Ride-Hailing
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
39
43% of problems
Expert
4
4% of problems
What You'll Practice
Trip performance analysis
Driver utilization metrics
Surge pricing analysis
Geographic demand patterns
Cohort retention
Earnings optimization
Match rate analysis
Supply-demand balance
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 High-Rated DriversPro
SQLEasy02Economy Long-Distance TripsPro
SQLEasy03Sedan Vehicles With Valid InspectionPro
SQLEasy04Premium Trips With Surge PricingPro
SQLEasy05Experienced Drivers With Phone NumberPro
SQLMedium06Trips With Rider DetailsPro
SQLEasy07Trips With Vehicle InfoPro
SQLMedium08Trip Revenue With Driver DetailsPro
SQLMedium09Rider Ratings With Trip ContextPro
SQLMedium10Drivers Without Completed ShiftsPro
SQLMedium11Completed Trips With Full ContextPro
SQLHard12Trip Volume by Service LevelPro
SQLEasy13Total Revenue by CityPro
SQLMedium14Average Fare by Vehicle TypePro
SQLMedium15Cancellation Rate by CityPro
SQLMedium16Top 5 Riders by Total SpendingPro
SQLHard17Drivers With High Cancellation RatePro
SQLHard18Rank Drivers by Trip CountPro
SQLMedium19Rider Trip Sequence NumberPro
SQLMedium20Daily Platform Revenue Running TotalPro
SQLHard21Highest Fare Trip per DriverPro
SQLHard22Driver Earnings 3-Trip Moving AveragePro
SQLHard23Fare Change From Previous TripPro
SQLHard24Trip Distance Quartile AnalysisPro
SQLHard25Drivers Above Average RatingPro
SQLMedium26Riders Who Used Economy and PremiumPro
SQLHard27Latest Trip per RiderPro
SQLHard28City Revenue With RankPro
SQLHard29Service Levels With Above-Average FarePro
SQLHard30October 2024 TripsPro
SQLEasy31Monthly Trip Volume TrendPro
SQLMedium32Average Trip Duration by Hour of DayPro
SQLMedium33Driver Wait Time by CityPro
SQLHard34Trips With Distance CategoryPro
SQLEasy35Payments With Fare Tier LabelPro
SQLMedium36Driver Activity Summary With Status LabelPro
SQLHard37Cities With Riders or DriversPro
SQLMedium38Drivers Registered Without TripsPro
SQLMedium39Driver Performance ScorecardPro
SQLHard40Rider Behavior AnalysisPro
SQLHard41City Operations DashboardPro
SQLHard42Surge Pricing Impact AnalysisPro
SQLHard43Cancellation Root Cause AnalysisPro
SQLHard44Payment Method Performance DashboardPro
SQLHard45Service Level Comparison ReportPro
SQLHard46Active Driver ProfilesPro
PYTHONEasy47Trip Status CountsPro
PYTHONEasy48Vehicle Type SummaryPro
PYTHONMedium49Rider Payment Preference SummaryPro
PYTHONMedium50Completed Economy TripsPro
PYTHONEasy51Long-Distance Premium TripsPro
PYTHONMedium52High-Rated Unbanned RidersPro
PYTHONMedium53Surge Trips With High FarePro
PYTHONHard54Trips Per Service LevelPro
PYTHONEasy55Total Revenue by Payment MethodPro
PYTHONMedium56Average Driver Rating by CityPro
PYTHONMedium57Trip Stats by City and Service LevelPro
PYTHONHard58Driver Earnings SummaryPro
PYTHONHard59Trips With Rider NamesPro
PYTHONEasy60Trip Payments With Driver InfoPro
PYTHONMedium61Drivers Without TripsPro
PYTHONMedium62Completed Trips With Vehicle and RatingPro
PYTHONHard63Full Trip Detail With All ContextPro
PYTHONHard64Rank Drivers by Trip CountPro
PYTHONMedium65Running Total Earnings Per DriverPro
PYTHONMedium667-Day Moving Average FarePro
PYTHONHard67Daily Trip Count Change vs Previous DayPro
PYTHONHard68Trip Request Hour and Day of WeekPro
PYTHONEasy69Trip Duration From TimestampsPro
PYTHONMedium70Monthly Trip Volume by Service LevelPro
PYTHONHard71Fill Missing Rider Phone NumbersPro
PYTHONEasy72Normalize Fare Components to PercentagePro
PYTHONMedium73Standardize Payment MethodsPro
PYTHONHard74Pivot Trip Counts by City and Service LevelPro
PYTHONMedium75Revenue Pivot by Payment Method and StatusPro
PYTHONHard76Classify Trips by Distance TierPro
PYTHONMedium77Trip Value ScorePro
PYTHONHard78Fare Per Km and Per MinutePro
PYTHONMedium79Driver Earnings Quartile BucketingPro
PYTHONHard80Time-Based Trip FeaturesPro
PYTHONHard81Driver Feature MatrixPro
PYTHONExpert82Trip Pricing Feature EngineeringPro
PYTHONHard83Fare Descriptive Statistics by Service LevelPro
PYTHONMedium84Distance vs Fare Correlation by Service LevelPro
PYTHONHard85Anomalous Fare Detection (IQR)Pro
PYTHONHard86Driver Performance ScorecardPro
PYTHONHard87Rider Lifetime Value ReportPro
PYTHONHard88City Supply-Demand DashboardPro
PYTHONExpert89Surge Pricing Impact AnalysisPro
PYTHONExpert90Complete Trip Lifecycle ReportPro
PYTHONExpertReady to practice Ride-Hailing?
90 SQL and Python challenges built from real ride-hailing data. Graded instantly in your browser — no setup required.