SQL code copied to buffer
Group tasks by:
Support the project.
- 1. Get the actors
- 2. Retrieve Actor Names
- 3. Ordered Movie Titles
- 4. Top 10 Movies by Title
- 5. Get films list third page
- 6. Sort Movies by Multiple Fields
- 7. Get the longest movie
- 8. Find long movies
- 9. Find Long Comedies
- 10. Classic movies
- 11. Retrieve Actors by Name
- 12. Find duplicate actor names
- 13. Most Popular Actor Surname
- 14. Get the languages list
- 15. Ordered Languages List
- 16. Top 5 Longest Films
- 17. Find stuff members by condition
- 18. Get the sorted list of films with condition
- 19. Find clients starting with the letter "A"
- 20. Find clients starting with the letter "A" (2)
- 21. Find customers full names
- 22. Find addresses using sub-query
- 23. Find addresses using JOIN
- 24. Find all the actors in the film
- 25. Find all films of an actor
- 26. Find clients who rented the film
- 27. Find all films where HENRY BERRY did not participate
- 28. Find the number of films in which the actor took part
- 29. Find actors more popular than HENRY BERRY
- 30. Find the distribution of films by category
- 31. Find the average length of a movie
- 32. Find minimum, maximum and average film duration
- 33. Find long movie categories
- 34. Find the minimal and maximal film rental cost
- 35. Find company stores details
- 36. Average film rental duration for each customer
- 37. Find the average length of a movie by category
- 38. The average cost of renting a movie by category
- 39. Find sad actors
- 40. Find the most diverse actors
- 41. Analyze monthly payment
- 42. Find the best month
- 43. Find the films never been rented
- 44. Find the most popular film
- 45. Analyze rental data for film
- 46. Find customers who did not return rented discs
- 47. Find average daily film rentals
- 48. Calculate daily income for the month
- 49. Find movie distribution by store
- 50. Find the distribution of customer activity
- 51. Find movie popularity rating
- 52. Quarterly earnings analysis
- 53. Find the countries with the most customers
- 54. Find film names by description
- 55. Find most active customers
- 56. Generate dates table
- 57. Calculate the number of days off in a month
- 58. Calculate factorial
- 59. Find average disk idle time
- 60. Find the distribution by categories
- 61. Find the average client activity time
- 62. Find the average revenue
- 63. Find the store average revenue
- 64. Analyze monthly payments (2)
- 65. Calculate the area of a circle
- 66. Calculate circle perimeter
- 67. Find customer details
- 68. Find EMILY DEE fans
- 69. Clients who are not familiar with EMILY DEE films
- 70. Find the count of rented disks
- 71. Find the number of returns
- 72. Disc rental and return statistics
- 73. Find active customers
- 74. Find the least popular movies
- 75. Find the top spending customers
- 76. Films Without Available Inventory
- 77. Find languages not represented in films
- 78. Find movies that have never been rented
- 79. Movies with above-average rental rates
- 80. Clients with a high number of rentals
- 81. Movies with the highest replacement cost
- 82. Films with the highest replacement cost
- 83. Count the rental delays
- 84. Calculate the percentage of delays
- 85. Get movie cast lists
- 86. Extract address and domain from email
- 87. Get a list of actors - namesakes
- 88. List Movies with Their Categories
- 89. Average Rental Duration
- 90. Films with Low Rental Time
- 91. Movie rental prices by category
- 92. Get rolling payment amounts
- 93. Find the number of films in each category
- 94. Get list of clients
- 95. Analyze customer payments
- 96. Unique Movie Ratings
- 97. Find the most diverse clients
- 98. Find actors duets
- 99. Find film distribution
- 100. Find films that were out of stock
- 101. Payments analyze
- 102. Improve payments analyze
- 103. Get list of tables
- 104. Get table columns data
- 105. Get list of indexes
- 106. Distribution of clients by weekday
- 107. Client Rentals by Time of Day
- 108. Improve distribution of clients by weekday
- 109. Movies without cast records
- 110. Movies without actors records
- 111. Find all the actors in the film
- 112. Actors Excluding NC-17 Films
- 113. Analyze weekly rentals
- 114. Average rentals count
- 115. Find repeat rentals
- 116. Find the Horror films fans
- 117. Find the distribution of clients by country
- 118. Get the restricted films list
- 119. Get list of restricted films
- 120. Find never been delayed films
- 121. Find most delayed films
- 122. Create department table
- 123. NC-17 Films about DBA
- 124. Films about Dogs or Cats
- 125. Clients whose first name is the last name of another client
- 126. Find clients who have met each other
- 127. Customers with matching name initials
- 128. Create new address record
- 129. Update the postal code
- 130. Update Canadian postal codes
- 131. Enter Woodridge postal code
- 132. Enter new staff record
- 133. Create customer addresses view
- 134. Movies in One Store
- 135. Find movies with no available copies
- 136. Staff performance analysis
- 137. Distribution of films by categories in JSON format
- 138. Generate a list of movies in JSON format
- 139. Remove customer records
- 140. Addresses Lacking Postal Codes
- 141. Addresses with Even Postal Codes
- 142. Category popularity analysis
- 143. Generate monthly bill
- 144. Build a overal email list
- 145. Build shared surnames list
- 146. Select clients without the letter "A"
- 147. Alter the staff table
- 148. Gap & Islands problem
- 149. Find customers who sow same films
- 150. Find films in several categories
- 151. Find last names with double letters
- 152. Analysis of the cost of film rental by category
- 153. Rental history
- 154. Find films to share
- 155. Film distribution by category and store
- 156. Delete films records
- 157. Rented Films
- 1. Get airports data
- 2. Get a list of airports
- 3. Find long-range aircraft
- 4. Find Boeing aircraft
- 5. Get Bookings by Date
- 6. Get flights departed from Domodedovo
- 7. Get a list of aircraft from Domodedovo
- 8. Aircraft usage analysis
- 9. Fare Conditions Types
- 10. Aircraft Lacking Business Class Seats
- 11. Find Aircraft with All Fare Conditions
- 12. Get counts of seats by class
- 13. Get rows and seats count
- 14. Calculate the number of seats on flight
- 15. Get the list of destination airports
- 16. Get a list of airports with direct connections
- 17. Get a list of airports without direct connections
- 18. Get a list of no-show passengers
- 19. Get a list of passengers
- 20. Find the flight delay
- 21. Get flights statistics
- 22. Rate airports
- 23. Find a list of flight options
- 24. Find the fastest flight
- 25. Calculate the daily number of flights
- 26. Obtain a list of passengers
- 27. Find average flight occupancy
- 28. Total Bookings Amount
- 29. Monthly Bookings Count
- 30. Find flight occupancy by fare
- 31. Get list of tables
- 32. Get information about the columns
- 33. Airports with one-way departures
- 34. Find airports relations
- 35. Find small airports
- 36. Get the passenger list
- 37. Get aircraft seat map
- 38. FInd the plane coordinates
- 39. Get a list of planes in the air
- 40. FInd the planes coordinates
- 41. Display a table of airports
- 42. Count departing passengers
- 43. Number of passengers with total
- 44. Display a table of departures
- 45. Get a list of airports with more than one direct flight
- 46. Distribution of flights by days of the week
- 47. Get list of tables (PostgreSQL)
- 48. Passenger names classification
- 49. Extract Airport Data as JSON
- 1. Display a list of departments
- 2. Find non-Dollar/Euro countries
- 3. A sub-departments list (JOIN)
- 4. Get a list of sub-departments
- 5. Find foreign employees
- 6. Find Employees by Department
- 7. Find employee salary
- 8. Employees with high salaries
- 9. Employees with Above-Average Salaries
- 10. Find the Managed Department
- 11. Employees on the Video Database Project
- 12. Staff Availability Report
- 13. Create a phonebook
- 14. Find all customers with unshipped orders
- 15. Find the number of employees
- 16. Find highly paid employees
- 17. Find employees by hire date
- 18. Get list of highly paid employees
- 19. Find highly paid employees
- 20. Reduced salaries
- 21. Find valuable employees
- 22. Salary Ratio Calculation
- 23. Create a salary rating
- 24. Jobs Without Specific Requirements
- 25. Orders shipped in the following month
- 26. Update project information
- 27. Find the median salary
- 28. Managed by Robert Nelson
- 29. Delete employee records
- 30. Overloaded employees
- 31. Update Job Salaries
- 32. Remove View from Database
- 33. Salary Bucketing
- 1. Product categories
- 2. Product List
- 3. Filtered list of products
- 4. Ten heaviest products
- 5. Get list of tables (SQL Server)
- 6. Find customers with even numbers
- 7. Find customers by phone prefix
- 8. Find duplicate phone numbers
- 9. Get unique customers list
- 10. Duplicate Emails
- 11. Get product category color counts
- 12. Top states by population
- 13. List of subcategories
- 14. List of categories
- 15. Root categories list
- 16. Subcategories count
- 17. Products catalog
- 18. Products by category distribution
- 19. Large categories
- 20. Mountain Bikes catalog
- 21. Prepare mailing list
- 22. Customers without orders
- 23. Who ordered red helmet?
- 24. Who ordered helmet?
- 25. What bought Jon Grande?
- 26. The most popular product
- 27. Most Frequent Co-Purchase
- 28. Most popular products
- 29. Not buyng customers
- 30. Average Sales Delay
- 31. Frequently Purchased Product Pairs
- 32. Sales by Category Percentage
- 33. Product Sales Analysis
- 34. Product Weight Buckets
- 1. Retrieve All Departments
- 2. Staff names
- 3. Sort the penguins
- 4. Penguins species
- 5. Find light weight penguins
- 6. Penguins list
- 7. Distribution penguins by islands
- 8. Population Distribution (Pivot)
- 9. Find small penguins
- 10. Find small penguins species
- 11. Medium sized bill Penguins
- 12. Small bill Penguins
- 13. Penguins with low body weight
- 14. Search by pattern
- 15. Flipper length to body mass rate
- 16. Penguins whose sex is unknown
- 17. Heavy penguins
- 18. Penguins with absent data
- 19. Penguins and Islands
- 20. Count the penguins
- 21. Island with the minimum penguins mass
- 22. The most populated island
- 23. Penguins distribution
- 24. Penguins stats table
- 25. Common penguin species
- 26. Penguins habitat
- 27. Penguins stats
- 28. Staff information
- 29. Delete records
- 30. Rank Penguins by Body Mass
- 31. Set Last Service Date
- 32. Missing Data
- 33. Refurbished machines
- 34. Data migration
- 35. Create Penguins Table
- 36. Combine Penguin Lists
- 37. Unique Penguin List
- 38. Filter Little Penguins
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Find the flipper length to body mass rate for all penguins from table. Output result with three columns: species, sex and flipper_length_to_body_mass_rate. Order it by ascending of calculated column.
Use SQLite syntax to write your answer. Descriptions of tables are provided in the right pane.
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Querynomicon (SQLite)
A compact database for learning the basics of SQL.The Querynomicon database tables:
department - table of departments.
- identDepartment ID
- nameDepartment name
- buildingBuilding name
| ident | name | building |
|---|---|---|
| gen | Genetics | Chesson |
little_penguins - table of little penguins.
- speciesPenguin species
- islandIsland of residence
- bill_length_mmBill length, mm
- bill_depth_mmBill depth, mm
- flipper_length_mmFlipper length, mm
- body_mass_gBody mass, g
- sexSex
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex |
|---|---|---|---|---|---|---|
| Gentoo | Biscoe | 52.1 | 17 | 230 | 5550 | MALE |
penguins - table of penguins.
- speciesPenguin species
- islandIsland of residence
- bill_length_mmBill length, mm
- bill_depth_mmBill depth, mm
- flipper_length_mmFlipper length, mm
- body_mass_gBody mass, g
- sexSex
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex |
|---|---|---|---|---|---|---|
| Gentoo | Biscoe | 52.1 | 17 | 230 | 5550 | MALE |
staff - table of employees.
- identEmployee number
- personalEmployee first name
- familyEmployee last name
- deptDepartment
- ageAge
| ident | personal | family | dept | age |
|---|---|---|---|---|
| 7 | Abram | Chokshi | gen | 23 |
machine - table of machines.
- identMachine ID
- nameMachine name
- detailsJSON with details
| ident | name | details |
|---|---|---|
| 1 | WY401 | {"acquired": "2023-05-01"} |
| 2 | Inphormex | {"acquired": "2021-07-15", "refurbished": "2023-10-22"} |
| 3 | AutoPlate 9000 | {"note": "needs software update"} |
