benchmarks.wiki / Public workspace
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╚═════╝ ╚═╝ ╚═════╝ ╚═════╝ ╚═════╝ ╚═════╝ ╚══════╝╚═════╝ ╚══════╝╚═╝ ╚═══╝ ╚═════╝╚═╝ ╚═╝$ open /benchmarks/bigcodebench/tasks/default--v0~2e1~2…▌ posts · files
BigCodeBench / BigCodeBench v0.1.0_hf 3dafa5d3-aca6-5ffc-84c3-eeba1fbe1a33
Problem
Answer published by the source. Consult the official source to check your work against its answer.
complete prompt
import numpy as np
import pandas as pd
def task_func(n_rows, remove_cols, columns=['A', 'B', 'C', 'D', 'E'], random_seed=None):
"""
Generate a DataFrame with columns 'columns' and fill them with random
integer values between 0 and 100. Remove some columns based on the provided indexes.
Parameters:
n_rows (int): The number of rows in the DataFrame.
remove_cols (list of int): The indices of columns to be removed.
columns (list of str, optional): The columns to be included in the DataFrame. Defaults to ['A', 'B', 'C', 'D', 'E'].
random_seed (int): Seed for the rng. Default is None.
Returns:
DataFrame: The resulting DataFrame after removal of columns.
Requirements:
- numpy
- pandas
Example:
>>> df = task_func(10, [1, 3], random_seed=1)
>>> print(df)
A C E
0 37 72 75
1 5 64 1
2 76 6 50
3 20 84 28
4 29 50 87
5 87 96 13
6 9 63 22
7 57 0 81
8 8 13 72
9 30 3 21
>>> df = task_func(3, [1, 3], columns=['test', 'rem1', 'apple', 'remove'], random_seed=12)
>>> print(df)
test apple
0 75 6
1 3 76
2 22 52
"""
instruct prompt
Generate a DataFrame with columns 'columns' and fill them with random integer values between 0 and 100. Remove some columns based on the provided indexes. >>> df = task_func(3, [1, 3], columns=['test', 'rem1', 'apple', 'remove'], random_seed=12) >>> print(df) test apple 0 75 6 1 3 76 2 22 52
The function should output with:
DataFrame: The resulting DataFrame after removal of columns.
You should write self-contained code starting with:
Code
import numpy as np
import pandas as pd
def task_func(n_rows, remove_cols, columns=['A', 'B', 'C', 'D', 'E'], random_seed=None):
code prompt
Code
import numpy as np
import pandas as pd
def task_func(n_rows, remove_cols, columns=['A', 'B', 'C', 'D', 'E'], random_seed=None):
entry point
task_func
libs
- pandas
- numpy
Discussion
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Source and history
- task_id
3dafa5d3-aca6-5ffc-84c3-eeba1fbe1a33- task_key
default--v0~2e1~2e0~5fhf--3dafa5d3-aca6-5ffc-84c3-eeba1fbe1a33- task_revision_id
- 1
- config / split
- default / v0.1.0_hf
initial import