Skip to content

datainpoint/final-programming-and-data-analysis-2024

BranchesTags

Repository files navigation

final-programming-and-data-analysis-2024

Final: Programming and Data Analysis 2024.

  • Define functions or classes in asgmt.py given 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.py to validate your functions or classes.
  • Upload asgmt.py to NTU COOL.

01. Define a class ManipulateArgs which instantiates object takes *args with three methods sort_asc(), sort_desc() and reverse().

class ManipulateArgs:
    """
    >>> manipulate_args = ManipulateArgs(3, 2, 7, 5, 11)
    >>> manipulate_args.sort_asc()
    [2, 3, 5, 7, 11]
    >>> manipulate_args.sort_desc()
    [11, 7, 5, 3, 2]
    >>> manipulate_args.reverse()
    [11, 5, 7, 2, 3]
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

02. Define a class SequenceGenerator which instantiates objects with two methods get_evens() and get_odds() that return a list with the first n evens or odds.

class SequenceGenerator:
    """
    >>> sequence_generator = SequenceGenerator(5)
    >>> sequence_generator.get_evens()
    [0, 2, 4, 6, 8]
    >>> sequence_generator.get_odds()
    [1, 3, 5, 7, 9]
    >>> sequence_generator = SequenceGenerator(7)
    >>> sequence_generator.get_evens()
    [0, 2, 4, 6, 8, 10, 12]
    >>> sequence_generator.get_odds()
    [1, 3, 5, 7, 9, 11, 13]
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

03. Define a function get_primes() which returns a list with the first n primes.

Source: https://en.wikipedia.org/wiki/Prime_number

def get_primes(n: int) -> list:
    """
    >>> get_primes(3)
    [2, 3, 5]
    >>> get_primes(5)
    [2, 3, 5, 7, 11]
    >>> get_primes(7)
    [2, 3, 5, 7, 11, 13, 17]
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

04. Define a function get_fibonacci() which returns a list with the first n Fibonacci integers (given n >= 2).

Source: https://en.wikipedia.org/wiki/Fibonacci_sequence

def get_fibonacci(n: int) -> list:
    """
    >>> get_fibonacci(3)
    [0, 1, 1]
    >>> get_fibonacci(5)
    [0, 1, 1, 2, 3]
    >>> get_fibonacci(7)
    [0, 1, 1, 2, 3, 5, 8]
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

05. Based on previous three questions, define a class AdvancedSequenceGenerator which instantiates objects with four methods get_evens(), get_odds(), get_primes(), and get_fibonacci() that return a list with the first n evens, odds, primes or integers of a Fibonacci sequence (given n >= 2).

class AdvancedSequenceGenerator:
    """
    >>> advanced_sequence_generator = AdvancedSequenceGenerator(5)
    >>> advanced_sequence_generator.get_evens()
    [0, 2, 4, 6, 8]
    >>> advanced_sequence_generator.get_odds()
    [1, 3, 5, 7, 9]
    >>> advanced_sequence_generator.get_primes()
    [2, 3, 5, 7, 11]
    >>> advanced_sequence_generator.get_fibonacci()
    [0, 1, 1, 2, 3]
    >>> advanced_sequence_generator = AdvancedSequenceGenerator(7)
    >>> advanced_sequence_generator.get_evens()
    [0, 2, 4, 6, 8, 10, 12]
    >>> advanced_sequence_generator.get_odds()
    [1, 3, 5, 7, 9, 11, 13]
    >>> advanced_sequence_generator.get_primes()
    [2, 3, 5, 7, 11, 13, 17]
    >>> advanced_sequence_generator.get_fibonacci()
    [0, 1, 1, 2, 3, 5, 8]
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

06. Define a class KeyValueMethods which instantiates object takes **kwargs with 3 methods reverse(), to_upper(), and to_lower().

class KeyValueMethods:
    """
    >>> key_value_methods = KeyValueMethods(twn="Taiwan")
    >>> key_value_methods.reverse()
    {'Taiwan': 'twn'}
    >>> key_value_methods.to_upper()
    {'TWN': 'TAIWAN'}
    >>> key_value_methods.to_lower()
    {'twn': 'taiwan'}
    >>> key_value_methods = KeyValueMethods(twn="Taiwan", jpn="Japan")
    >>> key_value_methods.reverse()
    {'Taiwan': 'twn', 'Japan': 'jpn'}
    >>> key_value_methods.to_upper()
    {'TWN': 'TAIWAN', 'JPN': 'JAPAN'}
    >>> key_value_methods.to_lower()
    {'twn': 'taiwan', 'jpn': 'japan'}
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

07. Define a function rotate_character() which rotates a character with the 13th letter after it.

Source: https://en.wikipedia.org/wiki/ROT13

def rotate_character(x: str) -> str:
    """
    >>> rotate_character("A")
    'N'
    >>> rotate_character("B")
    'O'
    >>> rotate_character("L")
    'Y'
    >>> rotate_character("M")
    'Z'
    >>> rotate_character("a")
    'n'
    >>> rotate_character("b")
    'o'
    >>> rotate_character("l")
    'y'
    >>> rotate_character("m")
    'z'
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

08. Define a function rotate_sentence() which rotates a sentence with ROT13.

Source: https://en.wikipedia.org/wiki/ROT13

def rotate_sentence(x: str) -> str:
    """
    >>> rotate_sentence("Abj vf orggre guna arire.")
    'Now is better than never.'
    >>> rotate_sentence("Now is better than never.")
    'Abj vf orggre guna arire.'
    >>> rotate_sentence("Rkcyvpvg vf orggre guna vzcyvpvg.")
    'Explicit is better than implicit.'
    >>> rotate_sentence("Explicit is better than implicit.")
    'Rkcyvpvg vf orggre guna vzcyvpvg.'
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

09. Based on previous two questions, define a class Rot13 which instantiates objects with 2 methods rotate_character() and rotate_sentence().

Source: https://en.wikipedia.org/wiki/ROT13

class Rot13:
    """
    >>> rot13 = Rot13()
    >>> rot13.rotate_character("A")
    'N'
    >>> rot13.rotate_character("B")
    'O'
    >>> rot13.rotate_character("L")
    'Y'
    >>> rot13.rotate_character("M")
    'Z'
    >>> rot13.rotate_character("a")
    'n'
    >>> rot13.rotate_character("b")
    'o'
    >>> rot13.rotate_character("l")
    'y'
    >>> rot13.rotate_character("m")
    'z'
    >>> rot13.rotate_sentence("Abj vf orggre guna arire.")
    'Now is better than never.'
    >>> rot13.rotate_sentence("Now is better than never.")
    'Abj vf orggre guna arire.'
    >>> rot13.rotate_sentence("Rkcyvpvg vf orggre guna vzcyvpvg.")
    'Explicit is better than implicit.'
    >>> rot13.rotate_sentence("Explicit is better than implicit.")
    'Rkcyvpvg vf orggre guna vzcyvpvg.'
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

10. Given a plain text file friends.txt in working directory, define a class Friends with 2 methods get_actor() and get_character().

class Friends:
    """
    >>> friends = Friends()
    >>> friends.get_actor("Rachel Green")
    'Jennifer Aniston'
    >>> friends.get_actor("Monica Geller")
    'Courteney Cox'
    >>> friends.get_actor("Phoebe Buffay")
    'Lisa Kudrow'
    >>> friends.get_character("Jennifer Aniston")
    'Rachel Green'
    >>> friends.get_character("Courteney Cox")
    'Monica Geller'
    >>> friends.get_character("Lisa Kudrow")
    'Phoebe Buffay'
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

11. Define a function import_mlb_teams_json() which imports mlb_teams.json in working directory.

def import_mlb_teams_json() -> list:
    """
    >>> mlb_teams_json = import_mlb_teams_json()
    >>> type(mlb_teams_json)
    list
    >>> len(mlb_teams_json)
    30
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

12. Define a function find_location_with_name() which returns a MLB team's location name (locationName) given team's full name (name) based on mlb_teams.json in working directory.

def find_location_with_name(name: str) -> str:
    """
    >>> find_location_with_name("New York Yankees")
    'Bronx'
    >>> find_location_with_name("New York Mets")
    'Flushing'
    >>> find_location_with_name("Los Angeles Dodgers")
    'Los Angeles'
    >>> find_location_with_name("Los Angeles Angels")
    'Anaheim'
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

13. Define a function find_names_with_franchise() which returns MLB team names (name) given team's franchise name (franchiseName) based on mlb_teams.json in working directory. If there are multiple teams in the same franchise, return as a tuple, otherwise return as a str.

def find_names_with_franchise(franchise: str):
    """
    >>> find_names_with_franchise("Los Angeles")
    ('Los Angeles Angels', 'Los Angeles Dodgers')
    >>> find_names_with_franchise("New York")
    ('New York Yankees', 'New York Mets')
    >>> find_names_with_franchise("Boston")
    'Boston Red Sox'
    >>> find_names_with_franchise("San Francisco")
    'San Francisco Giants'
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

14. Define a function import_imdb_files() which imports 3 CSV files movies.csv, movies_directors.csv, and directors.csv in working directory.

def import_imdb_files() -> tuple:
    """
    >>> movies, movies_directors, directors = import_imdb_files()
    >>> type(movies)
    pandas.core.frame.DataFrame
    >>> type(movies_directors)
    pandas.core.frame.DataFrame
    >>> type(directors)
    pandas.core.frame.DataFrame
    >>> movies.shape
    (250, 5)
    >>> movies_directors.shape
    (284, 4)
    >>> directors.shape
    (179, 3)
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

15. Define a function merge_dataframes() which merges(or joins) 3 dataframes based on the following join-keys movies.id, movies_directors.movie_id, movies_directors.director_id, and directors.id. Select specified columns showing movie's title, director's name and their directing order given movies.csv, movies_directors.csv, and directors.csv in working directory.

                        title              director  ord
0    The Shawshank Redemption        Frank Darabont    1
1              The Green Mile        Frank Darabont    1
2               The Godfather  Francis Ford Coppola    1
3       The Godfather Part II  Francis Ford Coppola    1
4              Apocalypse Now  Francis Ford Coppola    1
..                        ...                   ...  ...
279             The 400 Blows     François Truffaut    1
280                  The Help           Tate Taylor    1
281                   Aladdin          Ron Clements    1
282                   Aladdin           John Musker    2
283                  Drishyam       Nishikant Kamat    1

[284 rows x 3 columns]

def merge_dataframes() -> pd.core.frame.DataFrame:
    """
    >>> merged_dataframe = merge_dataframes()
    >>> type(merged_dataframe)
    pandas.core.frame.DataFrame
    >>> merged_dataframe.shape
    (284, 3)
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

16. Define a function find_movies_directed_by_nolan_spielberg() which finds out movies directed by Christopher Nolan and Steven Spielberg. Select specified columns showing movie's title and director's name given movies.csv, movies_directors.csv, and directors.csv in working directory.

             director                               title
0   Christopher Nolan                     The Dark Knight
1   Christopher Nolan                           Inception
2   Christopher Nolan                         Oppenheimer
3   Christopher Nolan                        Interstellar
4   Christopher Nolan                        The Prestige
5   Christopher Nolan                             Memento
6   Christopher Nolan               The Dark Knight Rises
7   Christopher Nolan                       Batman Begins
8    Steven Spielberg                    Schindler's List
9    Steven Spielberg                 Saving Private Ryan
10   Steven Spielberg             Raiders of the Lost Ark
11   Steven Spielberg  Indiana Jones and the Last Crusade
12   Steven Spielberg                       Jurassic Park
13   Steven Spielberg                 Catch Me If You Can
14   Steven Spielberg                                Jaws

def find_movies_directed_by_nolan_spielberg() -> pd.core.frame.DataFrame:
    """
    >>> movies_directed_by_nolan_spielberg = find_movies_directed_by_nolan_spielberg()
    >>> type(movies_directed_by_nolan_spielberg)
    pandas.core.frame.DataFrame
    >>> movies_directed_by_nolan_spielberg.shape
    (15, 2)
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

17. Define a function find_movies_with_multiple_directors() which finds out movies directed by multiple directors. Select specified columns showing movie's title, director's name and their directing order given movies.csv, movies_directors.csv, and directors.csv in working directory.

                                  title               director  ord
0                               Aladdin           Ron Clements    1
1                               Aladdin            John Musker    2
2                     Avengers: Endgame          Anthony Russo    1
3                     Avengers: Endgame              Joe Russo    2
4                Avengers: Infinity War          Anthony Russo    1
5                Avengers: Infinity War              Joe Russo    2
..                                  ...                    ...  ...
55                     The Wizard of Oz          Norman Taurog    4
56                     The Wizard of Oz         Richard Thorpe    5
57                     The Wizard of Oz             King Vidor    6
58                                   Up            Pete Docter    1
59                                   Up           Bob Peterson    2

[60 rows x 3 columns]

def find_movies_with_multiple_directors() -> pd.core.frame.DataFrame:
    """
    >>> movies_with_multiple_directors = find_movies_with_multiple_directors()
    >>> type(movies_with_multiple_directors)
    pandas.core.frame.DataFrame
    >>> movies_with_multiple_directors.shape
    (60, 3)
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

18. Define a function import_premier12_files() which imports 2 CSV files stats_batting.csv and stats_pitching.csv in working directory.

def import_premier12_files() -> tuple:
    """
    >>> stats_batting, stats_pitching = import_premier12_files()
    >>> type(stats_batting)
    pandas.core.frame.DataFrame
    >>> type(stats_pitching)
    pandas.core.frame.DataFrame
    >>> stats_batting.shape
    (164, 18)
    >>> stats_pitching.shape
    (166, 28)
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

19. Define a function find_batting_leaders() which calculates batting average AVG for batters from stats_batting.csv in working directory and finds out the batting leaders whose AVG is above .500. The formula for finding AVG is dividing a player's hits(H) by his total at-bats(AB).

Source: https://en.wikipedia.org/wiki/Batting_average_(baseball)

$$ AVG = \frac{H}{AB} $$

              Player Team       AVG   H  AB
0  CHEN, Chieh-Hsien  TPE  0.625000  15  24
1       HANSON, Alen  DOM  0.545455   6  11
2      PÉREZ, Hernán  VEN  0.533333   8  15
3     GAITAN, Alonso  MEX  0.526316  10  19

def find_batting_leaders() -> pd.core.frame.DataFrame:
    """
    >>> batting_leaders = find_batting_leaders()
    >>> type(batting_leaders)
    pandas.core.frame.DataFrame
    >>> batting_leaders.shape
    (4, 5)
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

20. Define a function find_pitching_leaders() which calculates earned run average ERA for pitchers from stats_pitching.csv in working directory and finds out the pitching leaders whose ERA is 0.00. The formula for finding ERA is: 9 x earned runs(ER) / innings pitched(IP).

Source: https://en.wikipedia.org/wiki/Earned_run_average

$$ ERA = 9 \times \frac{ER}{IP} $$

                Player Team  ERA    IP
0           HILL, Rich  USA  0.0  10.1
1            CHANG, Yi  TPE  0.0   8.0
2      FUJIHIRA, Shoma  JPN  0.0   6.0
3        CHEN, Kuan-Yu  TPE  0.0   5.2
4     KILOMÉ, Franklyn  DOM  0.0   5.0
5        HUANG, En-Sih  TPE  0.0   4.2
6   BALDONADO, Alberto  PAN  0.0   4.2
7       BENITEZ, Jorge  PUR  0.0   4.1
8          OTA, Taisei  JPN  0.0   4.0
9     SHIMIZU, Tatsuya  JPN  0.0   4.0
10       KIM, Seohyeon  KOR  0.0   4.0
11          GWAK, Been  KOR  0.0   4.0
12     PARK, Yeonghyun  KOR  0.0   3.2
13         CRUZ, Jesus  MEX  0.0   3.2
14      MIRANDA, Kevin  PAN  0.0   3.1
15    MENDEZ, Yohander  VEN  0.0   3.1
16     FUENTES, Steven  PAN  0.0   3.1
17        ALANIZ, R.J.  MEX  0.0   3.0
18        KENNEDY, Jon  AUS  0.0   3.0
19  RODRÍGUEZ, Ricardo  VEN  0.0   2.2
20     BURGOS, Enrique  PAN  0.0   2.1
21      REYES, Gerardo  MEX  0.0   2.1
22     MCGRATH, Daniel  AUS  0.0   2.0
23       WILKINS, Luke  AUS  0.0   2.0
24       SUZUKI, Shota  JPN  0.0   1.2
25    HUNTINGTON, Ryan  NED  0.0   1.2
26    RODRÍGUEZ, Joely  DOM  0.0   1.1
27  ESTANISTA, Jaydenn  NED  0.0   1.0
28       CINTRON, Luis  PUR  0.0   1.0
29       GOMEZ, Miguel  PAN  0.0   1.0
30    MARTINEZ, Raidel  CUB  0.0   1.0
31        SUZUKI, Sora  JPN  0.0   1.0
32        IBARRA, Jeff  MEX  0.0   0.2

def find_pitching_leaders() -> pd.core.frame.DataFrame:
    """
    >>> pitching_leaders = find_pitching_leaders()
    >>> type(pitching_leaders)
    pandas.core.frame.DataFrame
    >>> pitching_leaders.shape
    (33, 4)
    """
    ### BEGIN SOLUTION
    
    ### END SOLUTION

About

Final: Programming and Data Analysis 2024.

Resources

Readme
Activity
Custom properties

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages