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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
DataFrameavailable as
df
variable
-- Add your own queries here
SELECT *
FROM soccer.match
LIMIT 5
Explore Datasets
Use the match
, league
, and country
tables to explore the data and practice your skills!
- Use the
match
,league
, andcountry
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
andcountry
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')
prv_rev =
CALCULATE(
SUM([revenue]]),
PREVIOUSDAY([date])
)