NYC Taxi & Limousine Commission - yellow taxi trip records

Artikel
09/20/2022

The yellow 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

Microsoft provides Azure Open Datasets on an “as is” basis. Microsoft makes no warranties, express or implied, guarantees or conditions with respect to your use of the datasets. To the extent permitted under your local law, Microsoft disclaims all liability for any damages or losses, including direct, consequential, special, indirect, incidental or punitive, resulting from your use of the datasets.

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 1.5B rows (50 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	265	161 236	TLC Taxi Zone in which the taximeter was disengaged.
endLat	double	961,994	41.366138 40.75
endLon	double	1,144,935	-73.137393 -73.9824
extra	double	877	0.5 1.0	Miscellaneous extras and surcharges. Currently, this only includes the $0.50 and $1 rush hour and overnight charges.
fareAmount	double	18,935	6.5 4.5	The time-and-distance fare calculated by the meter.
improvementSurcharge	string	60	0.3 0	$0.30 improvement surcharge assessed trips at the flag drop. The improvement surcharge began being levied in 2015.
mtaTax	double	360	0.5 -0.5	$0.50 MTA tax that is automatically triggered based on the metered rate in use.
passengerCount	int	64	1 2	The number of passengers in the vehicle. This is a driver-entered value.
paymentType	string	6,282	CSH CRD	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.
puLocationId	string	266	237 161	TLC Taxi Zone in which the taximeter was engaged.
puMonth	int	12	3 5
puYear	int	29	2012 2011
rateCodeId	int	56	1 2	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.
startLat	double	833,016	41.366138 40.7741
startLon	double	957,428	-73.137393 -73.9821
storeAndFwdFlag	string	8	N 0	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	12,121	1.0 2.0	This field is automatically populated for credit card tips. Cash tips are not included.
tollsAmount	double	6,634	5.33 4.8	Total amount of all tolls paid in trip.
totalAmount	double	39,707	7.0 7.8	The total amount charged to passengers. Does not include cash tips.
tpepDropoffDateTime	timestamp	290,185,010	2010-11-07 01:29:00 2013-11-03 01:22:00	The date and time when the meter was disengaged.
tpepPickupDateTime	timestamp	289,948,585	2010-11-07 01:00:00 2009-11-01 01:05:00	The date and time when the meter was engaged.
tripDistance	double	14,003	1.0 0.9	The elapsed trip distance in miles reported by the taximeter.
vendorID	string	7	VTS CMT	A code indicating the TPEP provider that provided the record. 1= Creative Mobile Technologies, LLC; 2= VeriFone Inc.
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	tpepPickupDateTime	tpepDropoffDateTime	passengerCount	tripDistance	puLocationId	doLocationId	rateCodeId	storeAndFwdFlag	paymentType	fareAmount	mtaTax	improvementSurcharge	totalAmount	puYear	puMonth
2	1/24/2088 12:25:39 AM	1/24/2088 7:28:25 AM	1	4.05	24	162	1	N	2	14.5	0.5	0.3	15.3	2088	1
2	1/24/2088 12:15:42 AM	1/24/2088 12:19:46 AM	1	0.63	41	166	1	N	2	4.5	0.5	0.3	5.3	2088	1
2	11/4/2084 12:32:24 PM	11/4/2084 12:47:41 PM	1	1.34	238	236	1	N	2	10	0.5	0.3	10.8	2084	11
2	11/4/2084 12:25:53 PM	11/4/2084 12:29:00 PM	1	0.32	238	238	1	N	2	4	0.5	0.3	4.8	2084	11
2	11/4/2084 12:08:33 PM	11/4/2084 12:22:24 PM	1	1.85	236	238	1	N	2	10	0.5	0.3	10.8	2084	11
2	11/4/2084 11:41:35 AM	11/4/2084 11:59:41 AM	1	1.65	68	237	1	N	2	12.5	0.5	0.3	13.3	2084	11
2	11/4/2084 11:27:28 AM	11/4/2084 11:39:52 AM	1	1.07	170	68	1	N	2	9	0.5	0.3	9.8	2084	11
2	11/4/2084 11:19:06 AM	11/4/2084 11:26:44 AM	1	1.3	107	170	1	N	2	7.5	0.5	0.3	8.3	2084	11
2	11/4/2084 11:02:59 AM	11/4/2084 11:15:51 AM	1	1.85	113	137	1	N	2	10	0.5	0.3	10.8	2084	11
2	11/4/2084 10:46:05 AM	11/4/2084 10:50:09 AM	1	0.62	231	231	1	N	2	4.5	0.5	0.3	5.3	2084	11

Data access

Azure Notebooks

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

from datetime import datetime
from dateutil import parser


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

nyc_tlc_df.info()

# Pip install packages
import os, sys

!{sys.executable} -m pip install azure-storage-blob
!{sys.executable} -m pip install pyarrow
!{sys.executable} -m pip install pandas

# Azure storage access info
azure_storage_account_name = "azureopendatastorage"
azure_storage_sas_token = r""
container_name = "nyctlc"
folder_name = "yellow"

from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient

if azure_storage_account_name is None or azure_storage_sas_token is None:
    raise Exception(
        "Provide your specific name and key for your Azure Storage account--see the Prerequisites section earlier.")

print('Looking for the first parquet under the folder ' +
      folder_name + ' in container "' + container_name + '"...')
container_url = f"https://{azure_storage_account_name}.blob.core.windows.net/"
blob_service_client = BlobServiceClient(
    container_url, azure_storage_sas_token if azure_storage_sas_token else None)

container_client = blob_service_client.get_container_client(container_name)
blobs = container_client.list_blobs(folder_name)
sorted_blobs = sorted(list(blobs), key=lambda e: e.name, reverse=True)
targetBlobName = ''
for blob in sorted_blobs:
    if blob.name.startswith(folder_name) and blob.name.endswith('.parquet'):
        targetBlobName = blob.name
        break

print('Target blob to download: ' + targetBlobName)
_, filename = os.path.split(targetBlobName)
blob_client = container_client.get_blob_client(targetBlobName)
with open(filename, 'wb') as local_file:
    blob_client.download_blob().download_to_stream(local_file)

# Read the parquet file into Pandas data frame
import pandas as pd

print('Reading the parquet file into Pandas data frame')
df = pd.read_parquet(filename)

# you can add your filter at below
print('Loaded as a Pandas data frame: ')
df

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 NycTlcYellow

from datetime import datetime
from dateutil import parser


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

display(nyc_tlc_df.limit(5))

# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "nyctlc"
blob_relative_path = "yellow"
blob_sas_token = "r"

# Allow SPARK to read from Blob remotely
wasbs_path = 'wasbs://%s@%s.blob.core.windows.net/%s' % (blob_container_name, blob_account_name, blob_relative_path)
spark.conf.set(
  'fs.azure.sas.%s.%s.blob.core.windows.net' % (blob_container_name, blob_account_name),
  blob_sas_token)
print('Remote blob path: ' + wasbs_path)

# SPARK read parquet, note that it won't load any data yet by now
df = spark.read.parquet(wasbs_path)
print('Register the DataFrame as a SQL temporary view: source')
df.createOrReplaceTempView('source')

# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Azure Synapse

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

from datetime import datetime
from dateutil import parser

end_date = parser.parse('2018-06-06')
start_date = parser.parse('2018-05-01')
nyc_tlc = NycTlcYellow(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))

# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "nyctlc"
blob_relative_path = "yellow"
blob_sas_token = r""

# Allow SPARK to read from Blob remotely
wasbs_path = 'wasbs://%s@%s.blob.core.windows.net/%s' % (blob_container_name, blob_account_name, blob_relative_path)
spark.conf.set(
  'fs.azure.sas.%s.%s.blob.core.windows.net' % (blob_container_name, blob_account_name),
  blob_sas_token)
print('Remote blob path: ' + wasbs_path)

# SPARK read parquet, note that it won't load any data yet by now
df = spark.read.parquet(wasbs_path)
print('Register the DataFrame as a SQL temporary view: source')
df.createOrReplaceTempView('source')

# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Next steps

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