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BigCodeBench / BigCodeBench v0.1.0_hf d954534b-f57f-58ac-b566-ebf3140d4745

Problem

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

complete prompt

import pandas as pd
from sklearn.cluster import KMeans


def task_func(x_list, y_list, n_clusters=2, random_state=0):
    """
    Perform K-Means clustering on the given data by first turning it into a DataFrame with two columns "x" and "y" and then return the labels and centroids.

    Parameters:
    - x_list (list): List of data corresponding to 'x'
    - y_list (list): List of data corresponding to 'y'
    - n_clusters (int): Number of clusters to form, default to 2
    - random_state (int): Initial random state of k-means, default to 0

    Returns:
    tuple: The labels and centroids as numpy arrays.
        - kmeans.labels_: A NumPy array where each element is the cluster label assigned to each data point. 
        - kmeans.cluster_centers_: A NumPy array containing the coordinates of the cluster centers.

    Requirements:
    - pandas
    - sklearn

    Example:
    >>> df = pd.DataFrame({'x': [1, 2, 3, 4, 5, 6], 'y': [2, 3, 4, 5, 6, 7]})
    >>> labels, centroids = task_func([1, 2, 3, 4, 5, 6], [2, 3, 4, 5, 6, 7], 2, 0)
    """

instruct prompt

Perform K-Means clustering on the given data by first turning it into a DataFrame with two columns "x" and "y" and then return the labels and centroids.
The function should output with:
    tuple: The labels and centroids as numpy arrays.
    kmeans.labels_: A NumPy array where each element is the cluster label assigned to each data point.
    kmeans.cluster_centers_: A NumPy array containing the coordinates of the cluster centers.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.cluster import KMeans
def task_func(x_list, y_list, n_clusters=2, random_state=0):

code prompt

Code

import pandas as pd
from sklearn.cluster import KMeans
def task_func(x_list, y_list, n_clusters=2, random_state=0):

entry point

task_func

libs

  • pandas
  • sklearn

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Source and history

task_id
d954534b-f57f-58ac-b566-ebf3140d4745
task_key
default--v0~2e1~2e0~5fhf--d954534b-f57f-58ac-b566-ebf3140d4745
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import