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BigCodeBench / BigCodeBench v0.1.0_hf e86c829c-d365-57c8-9841-cb7992d82bd3

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
from sklearn.preprocessing import StandardScaler


def task_func(df):
    """
    Given a pandas DataFrame with random numeric values, run KMeans clusters on the data and return the labels.

    Parameters:
    - df (DataFrame): The DataFrame to use.

    Returns:
    - labels (np.array): The labels from the KMeans clustering.

    Requirements:
    - pandas
    - sklearn

    Example:
    >>> import numpy as np
    >>> np.random.seed(42)
    >>> df = pd.DataFrame(np.random.rand(500, 2) * 100, columns=['A', 'B']) 
    >>> labels = task_func(df)
    >>> print(labels)
    [0 2 1 0 2 0 2 1 0 1 1 1 0 0 1 1 0 2 1 2 0 0 0 0 1 2 2 2 1 1 1 2 0 0 0 1 0
     2 1 1 2 1 1 2 2 0 2 2 1 1 0 0 2 0 1 1 2 2 1 2 2 1 1 2 0 1 1 2 2 0 2 1 1 2
     1 2 0 2 2 0 0 2 0 1 0 1 1 1 2 2 1 2 0 2 1 0 2 1 2 2 1 0 1 0 1 2 1 1 0 2 2
     1 1 2 2 2 2 0 1 1 2 2 0 0 2 1 2 0 2 1 2 0 2 2 1 2 2 2 2 2 2 1 1 0 0 1 2 0
     1 1 0 2 2 1 2 1 0 2 1 1 2 1 2 2 1 0 1 1 2 1 1 1 0 1 0 0 1 0 0 2 0 0 2 2 1
     1 0 1 1 2 0 2 2 1 2 2 0 0 2 2 0 0 0 1 1 0 2 2 1 2 2 0 0 0 1 0 1 0 0 1 0 1
     2 2 1 2 0 0 0 1 0 2 2 0 0 0 0 0 0 2 2 0 2 1 2 0 1 1 1 2 2 0 1 2 2 2 2 1 0
     2 1 2 2 1 0 2 2 2 2 1 2 0 1 0 0 0 2 2 1 2 1 1 0 1 2 0 0 2 0 1 0 1 1 1 1 0
     1 2 1 1 1 1 0 1 0 0 1 2 1 2 1 1 1 0 1 2 2 0 1 1 1 1 0 2 2 0 2 1 1 2 0 1 1
     1 1 0 0 0 1 2 2 0 2 1 1 1 1 0 0 0 1 1 0 0 0 2 1 0 2 0 2 0 2 0 1 0 2 0 0 1
     1 2 0 0 2 0 1 0 2 2 1 0 0 2 0 0 1 1 0 2 2 1 0 1 0 0 2 0 2 2 1 2 0 2 1 2 0
     2 1 1 1 1 0 1 2 1 1 1 2 2 0 0 1 0 2 0 0 1 0 1 2 1 0 1 2 1 2 1 2 1 0 1 1 1
     1 2 2 1 0 1 1 0 0 2 1 1 2 1 0 1 2 2 1 0 1 0 2 1 0 0 0 2 1 0 2 2 0 1 1 0 0
     1 1 2 2 2 1 1 1 2 0 1 2 2 0 2 0 1 2 2]
    """

instruct prompt

Given a pandas DataFrame with random numeric values, run KMeans clusters on the data and return the labels.
The function should output with:
    labels (np.array): The labels from the KMeans clustering.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
def task_func(df):

code prompt

Code

import pandas as pd
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
def task_func(df):

entry point

task_func

libs

  • pandas
  • sklearn

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Source and history

task_id
e86c829c-d365-57c8-9841-cb7992d82bd3
task_key
default--v0~2e1~2e0~5fhf--e86c829c-d365-57c8-9841-cb7992d82bd3
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import