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Intermediate SQL


Data Manipulation in SQL

Here you can access every table used in the course. To access each table, you will need to specify the soccer schema in your queries (e.g., soccer.match for the match table, and soccer.league for the league table).

Note: When using sample integrations such as those that contain course data, you have read-only access. You can run queries, but cannot make any changes such as adding, deleting, or modifying the data (e.g., creating tables, views, etc.).

Take Notes

Add notes about the concepts you've learned and SQL cells with queries you want to keep.

Add your notes here

Unknown integration
DataFrameavailable as
-- Add your own queries here
FROM soccer.match
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

Explore Datasets

Use the match, league, and country tables to explore the data and practice your skills!

  • Use the match, league, and country tables to return the number of matches played in Great Britain versus elsewhere in the world.
    • "England", "Scotland", and "Wales" should be categorized as "Great Britain"
    • All other leagues will need to be categorized as "World".
  • Use the match and country tables to return the countries in which the average number of goals (home and away goals) scored are greater than the average number of goals of all matches.
  • In a soccer league, points are assigned to teams based on the result of a game. Here, let's assume that 3 points are awarded for a win, 1 for a tie, and 0 for a defeat. Use the match table to calculate the running total of points earned by the team "Chelsea" (team id 8455) in the season "2014/2015".
    • The final output should have the match date, the points earned by Chelsea, and the running total.
import pandas as pd
df = pd.read_excel('file.xlsx', sheet_name = 'Sheet1', usecols = 'A:D')

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