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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 
j.journey_type as journey_type,
sum(j.journeys_millions) as total_journeys_millions

FROM tfl.journeys as j

group by 1
order by 2 desc
LIMIT 6;
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DataFrameas
emirates_airline_popularity
variable
-- emirates_airline_popularity
SELECT DISTINCT
j.month,
j.year,
round(sum(j.journeys_millions),2) as rounded_journeys_millions

FROM tfl.journeys as j

where j.journey_type = 'Emirates Airline'

GROUP BY 1,2
HAVING (j.month is not null and j.year is not null and rounded_journeys_millions is not null)
ORDER BY 3 DESC
LIMIT 5;
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DataFrameas
least_popular_years_tube
variable
-- least_popular_years_tube
SELECT
j.year,
j.journey_type,
sum(j.journeys_millions) as total_journeys_millions

FROM tfl.journeys as j

WHERE j.journey_type = 'Underground & DLR'

GROUP BY 1,2
ORDER BY 3 ASC
LIMIT 5;