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London, or as the Romans called it "Londonium"! As of 2021, Greater London is home to over 8.5 million residents who speak over 300 languages. While the City of London is a little over one square mile (hence its nickname "The Square Mile"), Greater London has grown to encompass 32 boroughs spanning a total area of 606 square miles!

Given the city's roads were originally designed for horse and cart, this area and population growth have required the development of an efficient public transport system! Since the year 2000, this has been through the local government body called Transport for London, or TfL, which is managed by the London Mayor's office. Their remit covers the London Underground, Overground, Docklands Light Railway (DLR), buses, trams, river services (clipper and Emirates Airline cable car), roads, and even taxis.

The Mayor of London's office makes their data available to the public here. In this project, you will work with a slightly modified version of a dataset containing information about public transport journey volume by transport type.

The data has been loaded into a Databricks database containing a schema called tfl with a single table called journeys. The table, which you will use for the project, includes the following data:

tfl.journeys

ColumnDefinitionData type
monthMonth in number format, e.g., 1 equals Januaryinteger
yearYearinteger
daysNumber of days in the given monthinteger
report_dateDate that the data was reporteddate
journey_typeMethod of transport usedvarchar
journeys_millionsMillions of journeys, measured in decimalsfloat

You will execute SQL queries to answer three questions, as listed in the instructions.

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DataFrameas
most_popular_transport_types
variable
-- most_popular_transport_types
-- modify the query below as required
--SELECT *
--FROM tfl.journeys
--LIMIT 5;
SELECT
  journey_type,
  ROUND(SUM(journeys_millions), 2) AS total_journeys_millions
FROM
  tfl.journeys
WHERE
  journeys_millions IS NOT NULL
GROUP BY
  journey_type
ORDER BY
  total_journeys_millions DESC;
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DataFrameas
emirates_airline_popularity
variable
-- emirates_airline_popularity
SELECT
  month,
  year,
  ROUND(journeys_millions, 2) AS rounded_journeys_millions
FROM
  tfl.journeys
WHERE
  journey_type = 'Emirates Airline'
  AND journeys_millions IS NOT NULL
ORDER BY
  journeys_millions DESC
LIMIT 5;
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DataFrameas
least_popular_years_tube
variable
-- least_popular_years_tube
SELECT
  year,
  journey_type,
  ROUND(SUM(journeys_millions), 2) AS total_journeys_millions
FROM
  tfl.journeys
WHERE
  journey_type = 'Underground & DLR'
  AND journeys_millions IS NOT NULL
GROUP BY
  year, journey_type
ORDER BY
  total_journeys_millions ASC
LIMIT 5;