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BigCodeBench / BigCodeBench v0.1.0_hf 50e0ee88-f46d-5ca5-8ddb-3ee3208f9ae7

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(df, age: int, height: int):
    """
    Filters the input DataFrame based on specified 'Age' and 'Height' conditions and applies KMeans clustering.
    - If the filtered dataframe has less than 3  columns, add to it a column 'Cluster' with 0 for each row.
    - Otherwise, do a KMeans clustering (by Age and Height) with 3 clusters and add a column 'Cluster' to the dataframe which corresponds to the cluster
    index of the cluster to which each row belongs to.
    - Plot a scatter plot of the 'Age' and 'height' and colored by the cluster indices.
    - the xlabel should be 'Age', the ylabel 'Height' and the title 'KMeans Clustering based on Age and Height'.

    Parameters:
    df (DataFrame): The text to analyze.
    age (int): Filter out the rows of the dataframe which 'Age' value is less than or equal to this value.
    height (int): Filter out the rows of the dataframe which 'Height' value is greater than or equal to this value.

    Returns:
    DataFrame: The filtered dataframe with the new column.
    matplotlib.axes.Axes: The Axes object of the plotted data. If no KMeans was done, returns None.

    Requirements:
    - sklearn
    - matplotlib

    Example:
    >>> import pandas as pd
    >>> df = pd.DataFrame({
    ...     'Age': [30, 45, 60, 75],
    ...     'Height': [160, 170, 165, 190],
    ...     'Weight': [55, 65, 75, 85]
    ... })
    >>> selected_df, ax = task_func(df, 50, 180)
    >>> print(selected_df)
       Age  Height  Weight  Cluster
    2   60     165      75        0
    """

instruct prompt

Filters the input DataFrame based on specified 'Age' and 'Height' conditions and applies KMeans clustering. - If the filtered dataframe has less than 3  columns, add to it a column 'Cluster' with 0 for each row. - Otherwise, do a KMeans clustering (by Age and Height) with 3 clusters and add a column 'Cluster' to the dataframe which corresponds to the cluster index of the cluster to which each row belongs to. - Plot a scatter plot of the 'Age' and 'height' and colored by the cluster indices. - the xlabel should be 'Age', the ylabel 'Height' and the title 'KMeans Clustering based on Age and Height'.
The function should output with:
    DataFrame: The filtered dataframe with the new column.
    matplotlib.axes.Axes: The Axes object of the plotted data. If no KMeans was done, returns None.
You should write self-contained code starting with:

Code

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
def task_func(df, age: int, height: int):

code prompt

Code

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
def task_func(df, age: int, height: int):

entry point

task_func

libs

  • matplotlib
  • sklearn

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Source and history

task_id
50e0ee88-f46d-5ca5-8ddb-3ee3208f9ae7
task_key
default--v0~2e1~2e0~5fhf--50e0ee88-f46d-5ca5-8ddb-3ee3208f9ae7
task_revision_id
3
config / split
default / v0.1.0_hf

Official source

initial import