Assignment 6: Programming and Data Analysis 2024.
- Define functions or classes in
asgmt.pygiven their names, templates, and docstrings. - Use file name as a relative path rather than using an absolute path when you import data.
- Run
test-runner.pyto validate your functions or classes. - Upload
asgmt.pyto NTU COOL.
01. Define a function import_teams_json which imports the teams_nba.json as a dict in working directory.
def import_teams_nba_json() -> dict:
"""
>>> teams_nba_json = import_teams_nba_json()
>>> type(teams_nba_json)
dict
>>> len(teams_nba_json["data"])
30
"""
### BEGIN SOLUTION
### END SOLUTION02. Define a function find_teams_conference_division which returns a team's conference and division in a tuple given team's abbreviation, based on teams_nba.json in working directory.
def find_teams_conference_division(team_abb: str) -> tuple:
"""
>>> find_teams_conference_division("BOS")
('East', 'Atlantic')
>>> find_teams_conference_division("MIA")
('East', 'Southeast')
>>> find_teams_conference_division("LAL")
('West', 'Pacific')
>>> find_teams_conference_division("DEN")
('West', 'Northwest')
"""
### BEGIN SOLUTION
### END SOLUTION03. Define a function find_teams_city which returns a team's city given team's name, based on teams_nba.json in working directory.
def find_teams_city(team_name: str) -> str:
"""
>>> find_teams_city("Celtics")
'Boston'
>>> find_teams_city("Heat")
'Miami'
>>> find_teams_city("Lakers")
'Los Angeles'
>>> find_teams_city("Nuggets")
'Denver'
"""
### BEGIN SOLUTION
### END SOLUTION04. Define a function import_movies_csv() which creates a DataFrame of IMDb's top 250 rated movies of all time given movies.csv in working directory.
def import_movies_csv() -> pd.core.frame.DataFrame:
"""
>>> movies = import_movies_csv()
>>> type(movies)
pandas.core.frame.DataFrame
>>> movies.shape
(250, 5)
"""
### BEGIN SOLUTION
### END SOLUTION05. Define a function find_top_gun_maverick() which finds out "Top Gun: Maverick" given movies.csv in working directory.
def find_top_gun_maverick() -> pd.core.frame.DataFrame:
"""
>>> top_gun_maverick = find_top_gun_maverick()
>>> type(top_gun_maverick)
pandas.core.frame.DataFrame
>>> top_gun_maverick.shape
(1, 5)
>>> print(top_gun_maverick)
id title release_year runtime rating
126 127 Top Gun: Maverick 2022 130 8.3
"""
### BEGIN SOLUTION
### END SOLUTION06. Define a function find_starwars_episodes() which finds out "Star Wars" episodes given movies.csv in working directory.
def find_starwars_episodes() -> pd.core.frame.DataFrame:
"""
>>> starwars_episodes = find_starwars_episodes()
>>> type(starwars_episodes)
pandas.core.frame.DataFrame
>>> starwars_episodes.shape
(3, 5)
>>> print(starwars_episodes)
id title release_year runtime rating
15 16 Star Wars: Episode V - The Empire Strikes Back 1980 124 8.7
29 30 Star Wars: Episode IV - A New Hope 1977 121 8.6
91 92 Star Wars: Episode VI - Return of the Jedi 1983 131 8.3
"""
### BEGIN SOLUTION
### END SOLUTION07. Define a function import_team_taiwan() which creates a DataFrame of Taiwan's roster in Premier 12 2024 given team_taiwan.csv in working directory.
def import_team_taiwan() -> pd.core.frame.DataFrame:
"""
>>> team_taiwan = import_team_taiwan()
>>> type(team_taiwan)
pandas.core.frame.DataFrame
>>> team_taiwan.shape
(36, 7)
"""
### BEGIN SOLUTION
### END SOLUTION08. Define a function get_roster() which returns player or coach names as a list given team_taiwan.csv in working directory.
def get_roster(roster_type: str) -> list:
"""
>>> roster_players = get_roster("players")
>>> type(roster_players)
list
>>> len(roster_players)
28
>>> "CHEN Chieh-Hsien" in roster_players
True
>>> roster_coaches = get_roster("coaches")
>>> type(roster_coaches)
list
>>> len(roster_coaches)
8
>>> "PENG Cheng-Min" in roster_coaches
True
"""
### BEGIN SOLUTION
### END SOLUTION09. Define a function get_information() which returns player or coach information as a tuple based on his number given team_taiwan.csv in working directory.
def get_information(number: int) -> tuple:
"""
>>> get_information(24)
('CHEN Chieh-Hsien', 'OF', 'L/R', 173.0, 73.0, 1994.0)
>>> get_information(45)
('LIN Yu-Min', 'P', 'L/L', 180.0, 82.0, 2003.0)
>>> get_information(27)
('LIN Chia-Cheng', 'C', 'R/R', 185.0, 91.0, 1997.0)
>>> get_information(40)
('WANG Chien-Ming', 'Pitching Coach')
"""
### BEGIN SOLUTION
### END SOLUTION10. Define a class DataframeAttrs which instantiates objects with 3 methods get_shape(), get_dtypes(), and get_summary().
class DataframeAttrs:
"""
>>> movies = import_movies_csv()
>>> dfa = DataframeAttrs(movies)
>>> dfa.get_shape()
(250, 5)
>>> dfa.get_dtypes()
id int64
title object
release_year int64
runtime int64
rating float64
dtype: object
>>> dfa.get_summary()
id release_year runtime rating
count 250.000000 250.000000 250.000000 250.000000
mean 125.500000 1986.816000 129.052000 8.309600
std 72.312977 25.387086 29.916505 0.234538
min 1.000000 1921.000000 45.000000 8.000000
25% 63.250000 1966.250000 107.250000 8.100000
50% 125.500000 1994.500000 126.500000 8.200000
75% 187.750000 2007.000000 145.750000 8.400000
max 250.000000 2023.000000 238.000000 9.300000
>>> team_taiwan = import_team_taiwan()
>>> dfa = DataframeAttrs(team_taiwan)
>>> dfa.get_shape()
(36, 7)
>>> dfa.get_dtypes()
number int64
name object
postion object
bats_throws object
height float64
weight float64
birth_year float64
dtype: object
>>> dfa.get_summary()
number height weight birth_year
count 36.000000 28.000000 28.000000 28.000000
mean 46.555556 179.178571 82.607143 1996.678571
std 28.872572 4.455987 10.314820 3.151173
min 4.000000 168.000000 70.000000 1990.000000
25% 22.500000 175.000000 75.000000 1994.000000
50% 37.000000 179.000000 80.000000 1997.000000
75% 70.250000 182.250000 90.250000 2000.000000
max 99.000000 188.000000 110.000000 2003.000000
"""
### BEGIN SOLUTION
### END SOLUTION