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BigCodeBench / BigCodeBench v0.1.0_hf 6a149afb-ac30-53a9-b2f3-18a3cf28c50d

Problem

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

complete prompt

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


def task_func(df1, df2, column1="feature1", column2="feature2"):
    """Merge datasets, perform KMeans clustering, then return cluster labels and scatterplot.

    Each dataset is assumed to contain at least one id column and one feature column. The column to process
    is specified for df1 and df2 via column1 and column2, respectively. KMeans clustering is applied
    with k=2 and n_init=10. Resulting scatterplot shows column1 on the x-axis, column2 on the y-axis,
    and predicted cluster as color.

    Parameters:
    - df1 (pd.DataFrame): Dataframe with columns 'id' and feature columns including column1.
    - df2 (pd.DataFrame): Dataframe with columns 'id' and feature columns including column2.
    - column1 (str): Name of column containing features to model in df1. Defaults to "feature1".
    - column2 (str): Name of column containing features to model in df2. Defaults to "feature2".

    Returns:
    - labels (numpy.ndarray): Cluster labels for each data point (dtype=int32).
    - ax (matplotlib.axes._axes.Axes): The plotted figure's Axes object.

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

    Example:
    >>> df1 = pd.DataFrame({'id': [1, 2, 3], 'feature1': [1.2, 3.4, 5.6]})
    >>> df2 = pd.DataFrame({'id': [1, 2, 3], 'feature2': [2.3, 4.5, 6.7]})
    >>> labels, ax = task_func(df1, df2)
    >>> type(labels)
    <class 'numpy.ndarray'>
    >>> type(ax)
    <class 'matplotlib.axes._axes.Axes'>
    """

instruct prompt

Merge datasets, perform KMeans clustering, then return cluster labels and scatterplot. Each dataset is assumed to contain at least one id column and one feature column. The column to process is specified for df1 and df2 via column1 and column2, respectively. KMeans clustering is applied with k=2 and n_init=10. Resulting scatterplot shows column1 on the x-axis, column2 on the y-axis, and predicted cluster as color.
The function should output with:
    labels (numpy.ndarray): Cluster labels for each data point (dtype=int32).
    ax (matplotlib.axes._axes.Axes): The plotted figure's Axes object.
You should write self-contained code starting with:

Code

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
def task_func(df1, df2, column1="feature1", column2="feature2"):

code prompt

Code

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
def task_func(df1, df2, column1="feature1", column2="feature2"):

entry point

task_func

libs

  • matplotlib
  • sklearn

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Source and history

task_id
6a149afb-ac30-53a9-b2f3-18a3cf28c50d
task_key
default--v0~2e1~2e0~5fhf--6a149afb-ac30-53a9-b2f3-18a3cf28c50d
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import