Task 9:

Passenger Trip Analysis

DuckDB

Analyze the trip data from the yellow_tripdata table to gather insights based on the number of passengers. Your output should include the following columns:
- passenger_count: the number of passengers
- trip_count: the total number of trips for each passenger count
- average_distance: the average trip distance rounded to two decimal places
- median_distance: the median trip distance rounded to two decimal places
- distance_p90: the 90th percentile of trip distance rounded to two decimal places

Ensure that you filter for passenger_count values between 1 and 6 and exclude any trips with trip_distance as NULL. The results should be grouped by passenger_count and sorted in ascending order by this column.

Expected result Columns: 5 · Rows: 6

passenger_count	trip_count	average_distance	median_distance	distance_p90

Sample rows open after wrong checks: 0 of 2

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DuckDB yellow_tripdata Dataset

About DuckDB

DuckDB is an embedded analytical database designed for fast queries over local and application data.

It supports SQL and runs inside an application without a separate server process. Columnar storage and query execution make DuckDB well suited for analyzing large datasets, aggregations, and processing CSV and Parquet files.

In this playground, DuckDB is used to practice SQL with a taxi trip dataset.

yellow_tripdata is a learning dataset containing New York City yellow taxi trips.

The table is useful for practicing filtering, grouping, sorting, date operations, and aggregate calculations in DuckDB.

All fields in this dataset allow NULL values. No primary key or additional constraints are defined.

More about the NYC Yellow Taxi database: schema, sample queries and all exercises →

yellow_tripdata Table

yellow_tripdata - yellow taxi trips.

VendorID taxi service provider identifier.
tpep_pickup_datetime passenger pickup date and time.
tpep_dropoff_datetime passenger drop-off date and time.
passenger_count number of passengers.
trip_distance trip distance.
RatecodeID rate code identifier.
store_and_fwd_flag flag indicating that trip data was stored before forwarding.
PULocationID pickup zone identifier.
DOLocationID drop-off zone identifier.
payment_type payment method identifier.
fare_amount trip fare excluding additional charges.
extra additional charges.
mta_tax MTA tax.
tip_amount tip amount.
tolls_amount toll charges.
improvement_surcharge transportation system improvement surcharge.
total_amount total trip cost.
congestion_surcharge congestion surcharge.
Airport_fee airport fee.

yellow_tripdata table structure
Column name	Type	NULL	Key	Default	Extra
VendorID	INTEGER	YES	[null]	[null]	[null]
tpep_pickup_datetime	TIMESTAMP	YES	[null]	[null]	[null]
tpep_dropoff_datetime	TIMESTAMP	YES	[null]	[null]	[null]
passenger_count	BIGINT	YES	[null]	[null]	[null]
trip_distance	DOUBLE	YES	[null]	[null]	[null]
RatecodeID	BIGINT	YES	[null]	[null]	[null]
store_and_fwd_flag	VARCHAR	YES	[null]	[null]	[null]
PULocationID	INTEGER	YES	[null]	[null]	[null]
DOLocationID	INTEGER	YES	[null]	[null]	[null]
payment_type	BIGINT	YES	[null]	[null]	[null]
fare_amount	DOUBLE	YES	[null]	[null]	[null]
extra	DOUBLE	YES	[null]	[null]	[null]
mta_tax	DOUBLE	YES	[null]	[null]	[null]
tip_amount	DOUBLE	YES	[null]	[null]	[null]
tolls_amount	DOUBLE	YES	[null]	[null]	[null]
improvement_surcharge	DOUBLE	YES	[null]	[null]	[null]
total_amount	DOUBLE	YES	[null]	[null]	[null]
congestion_surcharge	DOUBLE	YES	[null]	[null]	[null]
Airport_fee	DOUBLE	YES	[null]	[null]	[null]