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BigCodeBench / BigCodeBench v0.1.0_hf 8bcf1753-3a1d-5010-a882-6901e8c2fb33
Problem
Answer published by the source. Consult the official source to check your work against its answer.
complete prompt
from random import randint, seed
import pandas as pd
# Method
def task_func(goals, penalties, rng_seed=None):
"""
Generate a Pandas DataFrame with colomns 'Team' and 'Match Result' of the results of football matches for multiple
teams, incorporating random goals and penalties. Penalties are converted into fines using a predefined cost.
Parameters:
- goals (int): The maximum number of goals a team can score in a match. Must be non-negative.
- penalties (int): The maximum number of penalties a team can receive in a match. Must be non-negative.
- rng_seed (int, optional): Seed for the random number generator to ensure reproducible results. Defaults to None.
Returns:
- pd.DataFrame: A pandas DataFrame with columns ['Team', 'Match Result'], detailing each team's goals and accumulated fines.
Requirements:
- pandas
- random
Example:
>>> seed(42) # Setting seed for reproducibility in this example
>>> results = task_func(5, 3, 42)
>>> print(results)
Team Match Result
0 Team A (5 goals, 2000)
2 Team C (1 goals, 0)
4 Team E (5 goals, $0)
"""
Plain-text mathematical notation (without MathML)
from random import randint, seed
import pandas as pd
# Method
def task_func(goals, penalties, rng_seed=None):
"""
Generate a Pandas DataFrame with colomns 'Team' and 'Match Result' of the results of football matches for multiple
teams, incorporating random goals and penalties. Penalties are converted into fines using a predefined cost.
Parameters:
- goals (int): The maximum number of goals a team can score in a match. Must be non-negative.
- penalties (int): The maximum number of penalties a team can receive in a match. Must be non-negative.
- rng_seed (int, optional): Seed for the random number generator to ensure reproducible results. Defaults to None.
Returns:
- pd.DataFrame: A pandas DataFrame with columns ['Team', 'Match Result'], detailing each team's goals and accumulated fines.
Requirements:
- pandas
- random
Example:
>>> seed(42) # Setting seed for reproducibility in this example
>>> results = task_func(5, 3, 42)
>>> print(results)
Team Match Result
0 Team A (5 goals, 0)1TeamB(0goals,2000)
2 Team C (1 goals, 1000)3TeamD(1goals,0)
4 Team E (5 goals, $0)
"""
Original LaTeX notation
from random import randint, seed
import pandas as pd
# Method
def task_func(goals, penalties, rng_seed=None):
"""
Generate a Pandas DataFrame with colomns 'Team' and 'Match Result' of the results of football matches for multiple
teams, incorporating random goals and penalties. Penalties are converted into fines using a predefined cost.
Parameters:
- goals (int): The maximum number of goals a team can score in a match. Must be non-negative.
- penalties (int): The maximum number of penalties a team can receive in a match. Must be non-negative.
- rng_seed (int, optional): Seed for the random number generator to ensure reproducible results. Defaults to None.
Returns:
- pd.DataFrame: A pandas DataFrame with columns ['Team', 'Match Result'], detailing each team's goals and accumulated fines.
Requirements:
- pandas
- random
Example:
>>> seed(42) # Setting seed for reproducibility in this example
>>> results = task_func(5, 3, 42)
>>> print(results)
Team Match Result
0 Team A (5 goals, $0)
1 Team B (0 goals, $2000)
2 Team C (1 goals, $1000)
3 Team D (1 goals, $0)
4 Team E (5 goals, $0)
"""
instruct prompt
Generate a Pandas DataFrame with colomns 'Team' and 'Match Result' of the results of football matches for multiple teams, incorporating random goals and penalties. Penalties are converted into fines using a predefined cost.
The function should output with:
pd.DataFrame: A pandas DataFrame with columns ['Team', 'Match Result'], detailing each team's goals and accumulated fines.
You should write self-contained code starting with:
Code
from random import randint, seed
import pandas as pd
# Method
def task_func(goals, penalties, rng_seed=None):
code prompt
Code
from random import randint, seed
import pandas as pd
# Method
def task_func(goals, penalties, rng_seed=None):
entry point
task_func
libs
- pandas
- random
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initial import