AU Simmons, BI Wauchope, HS Amano, T Dicks, LV Sutherland, WJ Dakos, V将上述数据中Au后边的数据按行读取存入列表

以下是将AU后面的数据按行读取存入列表的Python代码：

data = '''AU Simmons
BI Wauchope
HS Amano
T Dicks
LV Sutherland
WJ Dakos
V'''
lines = data.split('\n')
result = [line.split(' ', 1)[1] for line in lines if line.startswith('AU')]
print(result)

这段代码将会输出：['Simmons']

相关问题

对nba数据，统计不同的大学各队下面，人员的总人数，工资总和

很抱歉，作为AI语言模型，我无法直接访问和处理实时的NBA数据。但是，以下是一个可能的Python代码示例，根据假设的数据来统计不同大学的球队中的总人数和总工资。

假设有以下数据：

nba_data = [
    {'name': 'LeBron James', 'university': 'None', 'team': 'Los Angeles Lakers', 'salary': 37436858},
    {'name': 'Stephen Curry', 'university': 'Davidson', 'team': 'Golden State Warriors', 'salary': 43006362},
    {'name': 'Kevin Durant', 'university': 'Texas', 'team': 'Brooklyn Nets', 'salary': 40000000},
    {'name': 'Kawhi Leonard', 'university': 'San Diego State', 'team': 'Los Angeles Clippers', 'salary': 32742000},
    {'name': 'James Harden', 'university': 'Arizona State', 'team': 'Brooklyn Nets', 'salary': 40824000},
    {'name': 'Anthony Davis', 'university': 'Kentucky', 'team': 'Los Angeles Lakers', 'salary': 32742000},
    {'name': 'Russell Westbrook', 'university': 'UCLA', 'team': 'Washington Wizards', 'salary': 44211146},
    {'name': 'Damian Lillard', 'university': 'Weber State', 'team': 'Portland Trail Blazers', 'salary': 43869000},
    {'name': 'Joel Embiid', 'university': 'Kansas', 'team': 'Philadelphia 76ers', 'salary': 29542010},
    {'name': 'Ben Simmons', 'university': 'LSU', 'team': 'Philadelphia 76ers', 'salary': 17720000},
    {'name': 'DeMar DeRozan', 'university': 'USC', 'team': 'San Antonio Spurs', 'salary': 27739975},
    {'name': 'Devin Booker', 'university': 'Kentucky', 'team': 'Phoenix Suns', 'salary': 29434475},
    {'name': 'Ja Morant', 'university': 'Murray State', 'team': 'Memphis Grizzlies', 'salary': 9258000},
    {'name': 'Zion Williamson', 'university': 'Duke', 'team': 'New Orleans Pelicans', 'salary': 10245480},
    {'name': 'RJ Barrett', 'university': 'Duke', 'team': 'New York Knicks', 'salary': 8239200},
    {'name': 'LaMelo Ball', 'university': 'None', 'team': 'Charlotte Hornets', 'salary': 8199600}
]

则以下Python代码可以计算每个大学的总人数和总薪资：

from collections import defaultdict

# 创建一个字典，用于按大学名称存储球员信息
university_data = defaultdict(list)

# 将球员按大学名称存储
for player in nba_data:
    university_data[player['university']].append(player)

# 创建一个字典，用于存储每个大学的总人数和总薪资
totals_by_university = {}

# 计算每个大学的总人数和总薪资
for university, players in university_data.items():
    total_players = len(players)
    total_salary = sum(player['salary'] for player in players)
    totals_by_university[university] = {'total_players': total_players, 'total_salary': total_salary}

# 打印结果
for university, totals in totals_by_university.items():
    print(f'{university}: {totals["total_players"]} players, ${totals["total_salary"]}')

输出结果应为：

None: 2 players, $45636458
Davidson: 1 players, $43006362
Texas: 1 players, $40000000
San Diego State: 1 players, $32742000
Arizona State: 1 players, $40824000
Kentucky: 2 players, $62176475
UCLA: 1 players, $44211146
Weber State: 1 players, $43869000
Kansas: 1 players, $29542010
LSU: 1 players, $17720000
USC: 1 players, $27739975
Murray State: 1 players, $9258000
Duke: 2 players, $18484680

创建一个带有球员号码，球员名字，球员位置，入队时间，球队名称，球队代号，球员年薪的数据库表并且插入30个数据

好的，我理解了你的问题。以下是创建一个包含球员号码，球员名字，球员位置，入队时间，球队名称，球队代号，球员年薪的数据库表的 SQL 语句：

CREATE TABLE players (
  player_id INT PRIMARY KEY,
  player_name VARCHAR(50),
  player_position VARCHAR(50),
  joining_date DATE,
  team_name VARCHAR(50),
  team_code VARCHAR(10),
  player_salary INT
);

接下来，我会生成30个数据并插入到这个表中：

INSERT INTO players (player_id, player_name, player_position, joining_date, team_name, team_code, player_salary) VALUES
(1, 'LeBron James', 'SF', '2018-07-01', 'Los Angeles Lakers', 'LAL', 37436858),
(2, 'Kevin Durant', 'SF', '2019-07-01', 'Brooklyn Nets', 'BKN', 37199000),
(3, 'Stephen Curry', 'PG', '2017-07-01', 'Golden State Warriors', 'GSW', 37457154),
(4, 'Kawhi Leonard', 'SF', '2019-07-10', 'Los Angeles Clippers', 'LAC', 32742000),
(5, 'James Harden', 'SG', '2017-07-01', 'Houston Rockets', 'HOU', 37800000),
(6, 'Kyrie Irving', 'PG', '2019-07-01', 'Brooklyn Nets', 'BKN', 32742000),
(7, 'Chris Paul', 'PG', '2018-07-01', 'Oklahoma City Thunder', 'OKC', 38506482),
(8, 'Russell Westbrook', 'PG', '2018-07-01', 'Houston Rockets', 'HOU', 38506482),
(9, 'Paul George', 'SF', '2019-07-01', 'Los Angeles Clippers', 'LAC', 33005556),
(10, 'Jimmy Butler', 'SF', '2019-07-01', 'Miami Heat', 'MIA', 32742000),
(11, 'Klay Thompson', 'SG', '2019-07-01', 'Golden State Warriors', 'GSW', 32742000),
(12, 'Kemba Walker', 'PG', '2019-07-01', 'Boston Celtics', 'BOS', 32742000),
(13, 'Blake Griffin', 'PF', '2018-07-01', 'Detroit Pistons', 'DET', 34449964),
(14, 'Damian Lillard', 'PG', '2019-07-01', 'Portland Trail Blazers', 'POR', 29802321),
(15, 'Nikola Jokic', 'C', '2018-07-01', 'Denver Nuggets', 'DEN', 29542010),
(16, 'Joel Embiid', 'C', '2018-07-01', 'Philadelphia 76ers', 'PHI', 29542010),
(17, 'Ben Simmons', 'PG', '2019-07-01', 'Philadelphia 76ers', 'PHI', 29250000),
(18, 'Devin Booker', 'SG', '2018-07-01', 'Phoenix Suns', 'PHX', 27250000),
(19, 'John Wall', 'PG', '2019-07-01', 'Washington Wizards', 'WAS', 37800000),
(20, 'Bradley Beal', 'SG', '2019-07-01', 'Washington Wizards', 'WAS', 27734405),
(21, 'Mike Conley', 'PG', '2019-07-06', 'Utah Jazz', 'UTA', 32511623),
(22, 'Donovan Mitchell', 'SG', '2020-11-22', 'Utah Jazz', 'UTA', 5285000),
(23, 'Rudy Gobert', 'C', '2016-10-31', 'Utah Jazz', 'UTA', 25008427),
(24, 'DeMar DeRozan', 'SG', '2018-07-13', 'San Antonio Spurs', 'SAS', 27739975),
(25, 'LaMarcus Aldridge', 'PF', '2015-07-09', 'San Antonio Spurs', 'SAS', 24000000),
(26, 'Luka Doncic', 'SG', '2018-06-21', 'Dallas Mavericks', 'DAL', 9470400),
(27, 'Kristaps Porzingis', 'PF', '2019-02-01', 'Dallas Mavericks', 'DAL', 29000000),
(28, 'Deandre Ayton', 'C', '2018-06-21', 'Phoenix Suns', 'PHX', 9052920),
(29, 'Ja Morant', 'PG', '2019-06-20', 'Memphis Grizzlies', 'MEM', 9293640),
(30, 'Zion Williamson', 'PF', '2019-06-20', 'New Orleans Pelicans', 'NOP', 10245480);

请注意，这里的数据仅供示例，实际数据可能会有所不同。