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BigCodeBench / BigCodeBench v0.1.0_hf cb5e8849-4757-525e-b010-ab1ed0a59465

Problem

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

complete prompt

from collections import Counter
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt


def task_func(df, n_clusters=3, random_state=None, n_init=10):
    """
    Identify duplicate points in a DataFrame, perform KMeans clustering on the unique points,
    and record the clusters.

    Parameters:
    df (pd.DataFrame): A DataFrame containing at least two columns 'x' and 'y' representing points.
    n_clusters (int, optional): Number of clusters for KMeans clustering. Default is 3.
    random_state (int, optional): The seed used by the random number generator for reproducibility. Default is None.
    n_init (int, optional): Number of time the k-means algorithm will be run with different centroid seeds.
                            The final results will be the best output of n_init consecutive runs in terms of
                            within-cluster sum of squares. Default is 10.

    Returns:
    tuple: A tuple containing:
        - Counter: A Counter object with the count of duplicate points.
        - pd.DataFrame: A DataFrame with an additional column 'cluster' representing cluster assignments for unique points.
        - Axes: A scatter plot of the clustered data.

    Requirements:
    - collections.Counter
    - sklearn.cluster.KMeans
    - matplotlib.pyplot

    Example:
    >>> df = pd.DataFrame({\
            'x': [1, 2, 2, 2, 3, 4],\
            'y': [1, 1, 1, 1, 3, 3]\
        })
    >>> duplicates, df_clustered, ax = task_func(df, random_state=42)
    >>> df_clustered
       x  y  cluster
    0  1  1        2
    1  2  1        0
    4  3  3        1
    5  4  3        1
    >>> duplicates
    Counter({(2, 1): 3})
    """

instruct prompt

Identify duplicate points in a DataFrame, perform KMeans clustering on the unique points, and record the clusters.
The function should output with:
    tuple: A tuple containing:
    Counter: A Counter object with the count of duplicate points.
    pd.DataFrame: A DataFrame with an additional column 'cluster' representing cluster assignments for unique points.
    Axes: A scatter plot of the clustered data.
You should write self-contained code starting with:

Code

from collections import Counter
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
def task_func(df, n_clusters=3, random_state=None, n_init=10):

code prompt

Code

from collections import Counter
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
def task_func(df, n_clusters=3, random_state=None, n_init=10):

entry point

task_func

libs

  • collections
  • matplotlib
  • sklearn

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Source and history

task_id
cb5e8849-4757-525e-b010-ab1ed0a59465
task_key
default--v0~2e1~2e0~5fhf--cb5e8849-4757-525e-b010-ab1ed0a59465
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import