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NYC Taxi & Limousine Commission - green taxi trip records

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The green taxi trip records include fields capturing pick-up and drop-off dates/times, pick-up and drop-off locations, trip distances, itemized fares, rate types, payment types, and driver-reported passenger counts.

Note

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This dataset is provided under the original terms that Microsoft received source data. The dataset may include data sourced from Microsoft.

Volume and retention

This dataset is stored in Parquet format. There are about 80M rows (2 GB) in total as of 2018.

This dataset contains historical records accumulated from 2009 to 2018. You can use parameter settings in our SDK to fetch data within a specific time range.

Storage location

This dataset is stored in the East US Azure region. Allocating compute resources in East US is recommended for affinity.

Additional information

NYC Taxi and Limousine Commission (TLC):

The data was collected and provided to the NYC Taxi and Limousine Commission (TLC) by technology providers authorized under the Taxicab & Livery Passenger Enhancement Programs (TPEP/LPEP). The trip data was not created by the TLC, and TLC makes no representations as to the accuracy of these data.

View the original dataset location and the original terms of use.

Columns

Name Data type Unique Values (sample) Description
doLocationId string 264 74 42 DOLocationID TLC Taxi Zone in which the taximeter was disengaged.
dropoffLatitude double 109,721 40.7743034362793 40.77431869506836 Deprecated from 2016.07 onward
dropoffLongitude double 75,502 -73.95272827148438 -73.95274353027344 Deprecated from 2016.07 onward
extra double 202 0.5 1.0 Miscellaneous extras and surcharges. Currently, this only includes the $0.50 and $1 rush hour and overnight charges.
fareAmount double 10,367 6.0 5.5 The time-and-distance fare calculated by the meter.
improvementSurcharge string 92 0.3 0 $0.30 improvement surcharge assessed on hailed trips at the flag drop. The improvement surcharge began being levied in 2015.
lpepDropoffDatetime timestamp 58,100,713 2016-05-22 00:00:00 2016-05-09 00:00:00 The date and time when the meter was disengaged.
lpepPickupDatetime timestamp 58,157,349 2013-10-22 12:40:36 2014-08-09 15:54:25 The date and time when the meter was engaged.
mtaTax double 34 0.5 -0.5 $0.50 MTA tax that is automatically triggered based on the metered rate in use.
passengerCount int 10 1 2 The number of passengers in the vehicle. This is a driver-entered value.
paymentType int 5 2 1 A numeric code signifying how the passenger paid for the trip. 1= Credit card 2= Cash 3= No charge 4= Dispute 5= Unknown 6= Voided trip
pickupLatitude double 95,110 40.721351623535156 40.721336364746094 Deprecated from 2016.07 onward
pickupLongitude double 55,722 -73.84429931640625 -73.84429168701172 Deprecated from 2016.07 onward
puLocationId string 264 74 41 TLC Taxi Zone in which the taximeter was engaged.
puMonth int 12 3 5
puYear int 14 2015 2016
rateCodeID int 7 1 5 The final rate code in effect at the end of the trip. 1= Standard rate 2= JFK 3= Newark 4= Nassau or Westchester 5= Negotiated fare 6= Group ride
storeAndFwdFlag string 2 N Y This flag indicates whether the trip record was held in vehicle memory before sending to the vendor, also known as “store and forward,” because the vehicle did not have a connection to the server. Y= store and forward trip N= not a store and forward trip
tipAmount double 6,206 1.0 2.0 Tip amount – This field is automatically populated for credit card tips. Cash tips are not included.
tollsAmount double 2,150 5.54 5.76 Total amount of all tolls paid in trip.
totalAmount double 20,188 7.8 6.8 The total amount charged to passengers. Does not include cash tips.
tripDistance double 7,060 0.9 1.0 The elapsed trip distance in miles reported by the taximeter.
tripType int 3 1 2 A code indicating whether the trip was a street-hail or a dispatch that is automatically assigned based on the metered rate in use but can be altered by the driver. 1= Street-hail 2= Dispatch
vendorID int 2 2 1 A code indicating the LPEP provider that provided the record. 1= Creative Mobile Technologies, LLC; 2= VeriFone Inc.

Preview

vendorID lpepPickupDatetime lpepDropoffDatetime passengerCount tripDistance puLocationId doLocationId rateCodeID storeAndFwdFlag paymentType fareAmount extra mtaTax improvementSurcharge tipAmount tollsAmount totalAmount tripType puYear puMonth
2 6/24/2081 5:40:37 PM 6/24/2081 6:42:47 PM 1 16.95 93 117 1 N 1 52 1 0.5 0.3 0 2.16 55.96 1 2081 6
2 11/28/2030 12:19:29 AM 11/28/2030 12:25:37 AM 1 1.08 42 247 1 N 2 6.5 0 0.5 0.3 0 0 7.3 1 2030 11
2 11/28/2030 12:14:50 AM 11/28/2030 12:14:54 AM 1 0.03 42 42 5 N 2 5 0 0 0 0 0 5 2 2030 11
2 11/14/2020 11:38:07 AM 11/14/2020 11:42:22 AM 1 0.63 129 129 1 N 2 4.5 1 0.5 0.3 0 0 6.3 1 2020 11
2 11/14/2020 9:55:36 AM 11/14/2020 10:04:54 AM 1 3.8 82 138 1 N 2 12.5 1 0.5 0.3 0 0 14.3 1 2020 11
2 8/26/2019 4:18:37 PM 8/26/2019 4:19:35 PM 1 0 264 264 1 N 2 1 0 0.5 0.3 0 0 1.8 1 2019 8
2 7/1/2019 8:28:33 AM 7/1/2019 8:32:33 AM 1 0.71 7 7 1 N 1 5 0 0.5 0.3 1.74 0 7.54 1 2019 7
2 7/1/2019 12:04:53 AM 7/1/2019 12:21:56 AM 1 2.71 223 145 1 N 2 13 0.5 0.5 0.3 0 0 14.3 1 2019 7
2 7/1/2019 12:04:11 AM 7/1/2019 12:21:15 AM 1 3.14 166 142 1 N 2 14.5 0.5 0.5 0.3 0 0 18.55 1 2019 7
2 7/1/2019 12:03:37 AM 7/1/2019 12:09:27 AM 1 0.78 74 74 1 N 1 6 0.5 0.5 0.3 1.46 0 8.76 1 2019 7

Data access

Azure Notebooks

# This is a package in preview.
from azureml.opendatasets import NycTlcGreen

from datetime import datetime
from dateutil import parser


end_date = parser.parse('2018-06-06')
start_date = parser.parse('2018-05-01')
nyc_tlc = NycTlcGreen(start_date=start_date, end_date=end_date)
nyc_tlc_df = nyc_tlc.to_pandas_dataframe()

nyc_tlc_df.info()

Azure Databricks

# This is a package in preview.
# You need to pip install azureml-opendatasets in Databricks cluster. https://learn.microsoft.com/azure/data-explorer/connect-from-databricks#install-the-python-library-on-your-azure-databricks-cluster
from azureml.opendatasets import NycTlcGreen

from datetime import datetime
from dateutil import parser


end_date = parser.parse('2018-06-06')
start_date = parser.parse('2018-05-01')
nyc_tlc = NycTlcGreen(start_date=start_date, end_date=end_date)
nyc_tlc_df = nyc_tlc.to_spark_dataframe()

display(nyc_tlc_df.limit(5))

Azure Synapse

# This is a package in preview.
from azureml.opendatasets import NycTlcGreen

from datetime import datetime
from dateutil import parser


end_date = parser.parse('2018-06-06')
start_date = parser.parse('2018-05-01')
nyc_tlc = NycTlcGreen(start_date=start_date, end_date=end_date)
nyc_tlc_df = nyc_tlc.to_spark_dataframe()

# Display top 5 rows
display(nyc_tlc_df.limit(5))

# Display data statistic information
display(nyc_tlc_df, summary = True)

Next steps

View the rest of the datasets in the Open Datasets catalog.