Chapter 12 code and data
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-- FIRST EDITION FILE; IGNORE
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--------------------------------------------------------------
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-- Practical SQL: A Beginner's Guide to Storytelling with Data
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---------------------------------------------------------------------------
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-- Practical SQL: A Beginner's Guide to Storytelling with Data, 2nd Edition
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-- by Anthony DeBarros
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-- Chapter 12 Code Examples
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--------------------------------------------------------------
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----------------------------------------------------------------------------
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-- Listing 12-1: Extracting components of a timestamp value using date_part()
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SHOW ALL;
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SELECT
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date_part('year', '2019-12-01 18:37:12 EST'::timestamptz) AS "year",
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date_part('month', '2019-12-01 18:37:12 EST'::timestamptz) AS "month",
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date_part('day', '2019-12-01 18:37:12 EST'::timestamptz) AS "day",
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date_part('hour', '2019-12-01 18:37:12 EST'::timestamptz) AS "hour",
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date_part('minute', '2019-12-01 18:37:12 EST'::timestamptz) AS "minute",
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date_part('seconds', '2019-12-01 18:37:12 EST'::timestamptz) AS "seconds",
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date_part('timezone_hour', '2019-12-01 18:37:12 EST'::timestamptz) AS "tz",
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date_part('week', '2019-12-01 18:37:12 EST'::timestamptz) AS "week",
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date_part('quarter', '2019-12-01 18:37:12 EST'::timestamptz) AS "quarter",
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date_part('epoch', '2019-12-01 18:37:12 EST'::timestamptz) AS "epoch";
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date_part('year', '2022-12-01 18:37:12 EST'::timestamptz) AS year,
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date_part('month', '2022-12-01 18:37:12 EST'::timestamptz) AS month,
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date_part('day', '2022-12-01 18:37:12 EST'::timestamptz) AS day,
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date_part('hour', '2022-12-01 18:37:12 EST'::timestamptz) AS hour,
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date_part('minute', '2022-12-01 18:37:12 EST'::timestamptz) AS minute,
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date_part('seconds', '2022-12-01 18:37:12 EST'::timestamptz) AS seconds,
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date_part('timezone_hour', '2022-12-01 18:37:12 EST'::timestamptz) AS tz,
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date_part('week', '2022-12-01 18:37:12 EST'::timestamptz) AS week,
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date_part('quarter', '2022-12-01 18:37:12 EST'::timestamptz) AS quarter,
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date_part('epoch', '2022-12-01 18:37:12 EST'::timestamptz) AS epoch;
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SELECT '2022-12-01 18:37:12 EST'::timestamp AT TIME ZONE 'US/Pacific'
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-- Bonus: Using the SQL-standard extract() for similar datetime parsing:
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SELECT extract('year' from '2019-12-01 18:37:12 EST'::timestamptz) AS "year";
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SELECT extract('year' from '2022-12-01 18:37:12 EST'::timestamptz) AS year;
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-- Listing 12-2: Three functions for making datetimes from components
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-- make a date
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SELECT make_date(2018, 2, 22);
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SELECT make_date(2022, 2, 22);
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-- make a time
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SELECT make_time(18, 4, 30.3);
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-- make a timestamp with time zone
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SELECT make_timestamptz(2018, 2, 22, 18, 4, 30.3, 'Europe/Lisbon');
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SELECT make_timestamptz(2022, 2, 22, 18, 4, 30.3, 'Europe/Lisbon');
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-- Bonus: Retrieving the current date and time
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SELECT
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current_timestamp,
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localtimestamp,
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current_date,
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current_time,
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current_timestamp,
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localtime,
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localtimestamp,
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now();
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-- Listing 12-3: Comparing current_timestamp and clock_timestamp() during row insert
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CREATE TABLE current_time_example (
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time_id bigserial,
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time_id integer GENERATED ALWAYS AS IDENTITY,
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current_timestamp_col timestamp with time zone,
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clock_timestamp_col timestamp with time zone
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);
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INSERT INTO current_time_example (current_timestamp_col, clock_timestamp_col)
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INSERT INTO current_time_example
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(current_timestamp_col, clock_timestamp_col)
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(SELECT current_timestamp,
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clock_timestamp()
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FROM generate_series(1,1000));
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@ -63,32 +65,34 @@ SELECT * FROM current_time_example;
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-- Time Zones
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-- Listing 12-4: Showing your PostgreSQL server's default time zone
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-- Listing 12-4: Viewing your current time zone setting
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SHOW timezone; -- Note: You can see all run-time defaults with SHOW ALL;
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-- Listing 12-5: Showing time zone abbreviations and names
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SELECT * FROM pg_timezone_abbrevs;
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SELECT * FROM pg_timezone_names;
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SELECT * FROM pg_timezone_abbrevs ORDER BY abbrev;
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SELECT * FROM pg_timezone_names ORDER BY name;
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-- Filter to find one
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SELECT * FROM pg_timezone_names
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WHERE name LIKE 'Europe%';
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WHERE name LIKE 'Europe%'
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ORDER BY name;
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-- Listing 12-6: Setting the time zone for a client session
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SET timezone TO 'US/Pacific';
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SET TIME ZONE 'US/Pacific';
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CREATE TABLE time_zone_test (
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test_date timestamp with time zone
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);
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INSERT INTO time_zone_test VALUES ('2020-01-01 4:00');
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INSERT INTO time_zone_test VALUES ('2023-01-01 4:00');
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INSERT INTO time_zone_test VALUES ('2023-07-01 4:00');
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SELECT test_date
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FROM time_zone_test;
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SET timezone TO 'US/Eastern';
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SET TIME ZONE 'US/Eastern';
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SELECT test_date
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FROM time_zone_test;
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@ -99,28 +103,28 @@ FROM time_zone_test;
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-- Math with dates!
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SELECT '9/30/1929'::date - '9/27/1929'::date;
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SELECT '9/30/1929'::date + '5 years'::interval;
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SELECT '1929-09-30'::date - '1929-09-27'::date;
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SELECT '1929-09-30'::date + '5 years'::interval;
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-- Taxi Rides
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-- Listing 12-7: Creating a table and importing NYC yellow taxi data
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CREATE TABLE nyc_yellow_taxi_trips_2016_06_01 (
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trip_id bigserial PRIMARY KEY,
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vendor_id varchar(1) NOT NULL,
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CREATE TABLE nyc_yellow_taxi_trips (
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trip_id bigint GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
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vendor_id text NOT NULL,
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tpep_pickup_datetime timestamp with time zone NOT NULL,
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tpep_dropoff_datetime timestamp with time zone NOT NULL,
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passenger_count integer NOT NULL,
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trip_distance numeric(8,2) NOT NULL,
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pickup_longitude numeric(18,15) NOT NULL,
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pickup_latitude numeric(18,15) NOT NULL,
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rate_code_id varchar(2) NOT NULL,
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store_and_fwd_flag varchar(1) NOT NULL,
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rate_code_id text NOT NULL,
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store_and_fwd_flag text NOT NULL,
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dropoff_longitude numeric(18,15) NOT NULL,
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dropoff_latitude numeric(18,15) NOT NULL,
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payment_type varchar(1) NOT NULL,
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payment_type text NOT NULL,
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fare_amount numeric(9,2) NOT NULL,
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extra numeric(9,2) NOT NULL,
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mta_tax numeric(5,2) NOT NULL,
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@ -130,7 +134,7 @@ CREATE TABLE nyc_yellow_taxi_trips_2016_06_01 (
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total_amount numeric(9,2) NOT NULL
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);
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COPY nyc_yellow_taxi_trips_2016_06_01 (
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COPY nyc_yellow_taxi_trips (
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vendor_id,
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tpep_pickup_datetime,
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tpep_dropoff_datetime,
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@ -151,20 +155,23 @@ COPY nyc_yellow_taxi_trips_2016_06_01 (
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improvement_surcharge,
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total_amount
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)
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FROM 'C:\YourDirectory\yellow_tripdata_2016_06_01.csv'
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WITH (FORMAT CSV, HEADER, DELIMITER ',');
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-- FROM 'C:\YourDirectory\nyc_yellow_taxi_trips.csv'
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FROM '/Users/adebarros/Dropbox/DataMonky/Book-Writing/PracticalSQL_2e/Code-Repo/Chapter_12/nyc_yellow_taxi_trips.csv'
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WITH (FORMAT CSV, HEADER);
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CREATE INDEX tpep_pickup_idx
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ON nyc_yellow_taxi_trips_2016_06_01 (tpep_pickup_datetime);
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ON nyc_yellow_taxi_trips (tpep_pickup_datetime);
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SELECT count(*) FROM nyc_yellow_taxi_trips_2016_06_01;
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SELECT count(*) FROM nyc_yellow_taxi_trips;
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-- Listing 12-8: Counting taxi trips by hour
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SET TIME ZONE 'US/Eastern'; -- Set this if your PostgreSQL server defaults to a time zone other than US/Eastern
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SELECT
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date_part('hour', tpep_pickup_datetime) AS trip_hour,
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count(*)
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FROM nyc_yellow_taxi_trips_2016_06_01
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FROM nyc_yellow_taxi_trips
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GROUP BY trip_hour
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ORDER BY trip_hour;
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@ -174,12 +181,13 @@ COPY
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(SELECT
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date_part('hour', tpep_pickup_datetime) AS trip_hour,
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count(*)
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FROM nyc_yellow_taxi_trips_2016_06_01
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FROM nyc_yellow_taxi_trips
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GROUP BY trip_hour
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ORDER BY trip_hour
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)
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TO 'C:\YourDirectory\hourly_pickups_2016_06_01.csv'
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WITH (FORMAT CSV, HEADER, DELIMITER ',');
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-- TO 'C:\YourDirectory\hourly_taxi_pickups.csv'
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TO '/Users/adebarros/Desktop/hourly_taxi_pickups.csv'
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WITH (FORMAT CSV, HEADER);
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-- Listing 12-10: Calculating median trip time by hour
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percentile_cont(.5)
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WITHIN GROUP (ORDER BY
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tpep_dropoff_datetime - tpep_pickup_datetime) AS median_trip
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FROM nyc_yellow_taxi_trips_2016_06_01
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FROM nyc_yellow_taxi_trips
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GROUP BY trip_hour
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ORDER BY trip_hour;
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-- Listing 12-11: Creating a table to hold train trip data
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SET timezone TO 'US/Central';
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CREATE TABLE train_rides (
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trip_id bigserial PRIMARY KEY,
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segment varchar(50) NOT NULL,
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trip_id bigint GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
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segment text NOT NULL,
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departure timestamp with time zone NOT NULL,
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arrival timestamp with time zone NOT NULL
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);
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INSERT INTO train_rides (segment, departure, arrival)
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VALUES
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('Chicago to New York', '2017-11-13 21:30 CST', '2017-11-14 18:23 EST'),
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('New York to New Orleans', '2017-11-15 14:15 EST', '2017-11-16 19:32 CST'),
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('New Orleans to Los Angeles', '2017-11-17 13:45 CST', '2017-11-18 9:00 PST'),
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('Los Angeles to San Francisco', '2017-11-19 10:10 PST', '2017-11-19 21:24 PST'),
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('San Francisco to Denver', '2017-11-20 9:10 PST', '2017-11-21 18:38 MST'),
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('Denver to Chicago', '2017-11-22 19:10 MST', '2017-11-23 14:50 CST');
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('Chicago to New York', '2020-11-13 21:30 CST', '2020-11-14 18:23 EST'),
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('New York to New Orleans', '2020-11-15 14:15 EST', '2020-11-16 19:32 CST'),
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('New Orleans to Los Angeles', '2020-11-17 13:45 CST', '2020-11-18 9:00 PST'),
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('Los Angeles to San Francisco', '2020-11-19 10:10 PST', '2020-11-19 21:24 PST'),
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('San Francisco to Denver', '2020-11-20 9:10 PST', '2020-11-21 18:38 MST'),
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('Denver to Chicago', '2020-11-22 19:10 MST', '2020-11-23 14:50 CST');
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SET TIME ZONE 'US/Central';
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SELECT * FROM train_rides;
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Can't render this file because it is too large.
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@ -729,3 +729,70 @@ FROM pls_fy2018_libraries
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WHERE popu_lsa >= 250000;
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----------------------------------------------------------------------------
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-- Chapter 12: Working with Dates and Times
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----------------------------------------------------------------------------
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-- 1. Using the New York City taxi data, calculate the length of each ride using
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-- the pickup and drop-off timestamps. Sort the query results from the longest
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-- ride to the shortest. Do you notice anything about the longest or shortest
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-- trips that you might want to ask city officials about?
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-- Answer: More than 480 of the trips last more than 10 hours, which seems
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-- excessive. Moreover, two records have drop-off times before the pickup time,
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-- and several have pickup and drop-off times that are the same. It's worth
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-- asking whether these records have timestamp errors.
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SELECT
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trip_id,
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tpep_pickup_datetime,
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tpep_dropoff_datetime,
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tpep_dropoff_datetime - tpep_pickup_datetime AS length_of_ride
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FROM nyc_yellow_taxi_trips
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ORDER BY length_of_ride DESC;
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-- 2. Using the AT TIME ZONE keywords, write a query that displays the date and
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-- time for London, Johannesburg, Moscow, and Melbourne the moment January 1,
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-- 2100, arrives in New York City.
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-- Answer:
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SELECT '2100-01-01 00:00:00-05' AT TIME ZONE 'US/Eastern' AS new_york,
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'2100-01-01 00:00:00-05' AT TIME ZONE 'Europe/London' AS london,
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'2100-01-01 00:00:00-05' AT TIME ZONE 'Africa/Johannesburg' AS johannesburg,
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'2100-01-01 00:00:00-05' AT TIME ZONE 'Europe/Moscow' AS moscow,
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'2100-01-01 00:00:00-05' AT TIME ZONE 'Australia/Melbourne' AS melbourne;
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-- 3. As a bonus challenge, use the statistics functions in Chapter 11 to
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-- calculate the correlation coefficient and r-squared values using trip time
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-- and the total_amount column in the New York City taxi data, which represents
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-- total amount charged to passengers. Do the same with trip_distance and
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-- total_amount. Limit the query to rides that last three hours or less.
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-- Answer:
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SELECT
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round(
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corr(total_amount, (
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date_part('epoch', tpep_dropoff_datetime) -
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date_part('epoch', tpep_pickup_datetime)
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))::numeric, 2
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) AS amount_time_corr,
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round(
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regr_r2(total_amount, (
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date_part('epoch', tpep_dropoff_datetime) -
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date_part('epoch', tpep_pickup_datetime)
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))::numeric, 2
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) AS amount_time_r2,
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round(
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corr(total_amount, trip_distance)::numeric, 2
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) AS amount_distance_corr,
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round(
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regr_r2(total_amount, trip_distance)::numeric, 2
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) AS amount_distance_r2
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FROM nyc_yellow_taxi_trips
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WHERE tpep_dropoff_datetime - tpep_pickup_datetime <= '3 hours'::interval;
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-- Note: Both correlations are strong, with r values of 0.80 or higher. We'd
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-- expect this given the cost of a taxi ride is based on both time and distance.
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