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BigCodeBench / BigCodeBench v0.1.0_hf efe12992-d8e4-5fb5-bc5c-02480e60bc35

Problem

Answer published by the source. Consult the official source to check your work against its answer.

complete prompt

import pandas as pd
import numpy as np
from sklearn.decomposition import PCA


def task_func(array: list, random_seed: int = 42) -> (pd.DataFrame, np.ndarray):
    """
    Converts a 2D list into a pandas DataFrame and applies PCA for dimensionality reduction.

    This function creates a DataFrame from the provided 2D list and then applies PCA to reduce the dataset
    to its two main components. The function uses a fixed random seed to ensure reproducibility.

    Parameters:
    - array (list of list of int): A 2D list representing data rows and columns.
    - random_seed (int, optional): The seed for the random number generator. Default is 42.

    Returns:
    - pd.DataFrame: The original data in DataFrame format.
    - np.ndarray: The data after PCA transformation.

    Requirements:
    - pandas
    - numpy
    - sklearn.decomposition.PCA

    Examples:
    >>> data = [[1,2,3,4,5], [6,7,8,9,10], [11,12,13,14,15]]
    >>> df, transformed = task_func(data)
    >>> print(df)
        0   1   2   3   4
    0   1   2   3   4   5
    1   6   7   8   9  10
    2  11  12  13  14  15
    >>> print(transformed[:, 0])
    [ 11.18033989  -0.         -11.18033989]
    """

instruct prompt

Converts a 2D list into a pandas DataFrame and applies PCA for dimensionality reduction. This function creates a DataFrame from the provided 2D list and then applies PCA to reduce the dataset to its two main components. The function uses a fixed random seed to ensure reproducibility.
The function should output with:
    pd.DataFrame: The original data in DataFrame format.
    np.ndarray: The data after PCA transformation.
You should write self-contained code starting with:

Code

import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
def task_func(array: list, random_seed: int = 42) -> (pd.DataFrame, np.ndarray):

code prompt

Code

import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
def task_func(array: list, random_seed: int = 42) -> (pd.DataFrame, np.ndarray):

entry point

task_func

libs

  • pandas
  • numpy
  • sklearn

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Source and history

task_id
efe12992-d8e4-5fb5-bc5c-02480e60bc35
task_key
default--v0~2e1~2e0~5fhf--efe12992-d8e4-5fb5-bc5c-02480e60bc35
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import