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BigCodeBench / BigCodeBench v0.1.0_hf 764c7ab1-a29d-5ff7-ae7e-e08b08b2870c

Problem

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

complete prompt

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler


def task_func(data: pd.DataFrame) -> (pd.DataFrame, list):
    """
    This function takes a pandas DataFrame and standardizes its features using sklearn's StandardScaler,
    which standardizes features by removing the mean and scaling to unit variance.
    After standardization, it draws a histogram for each feature with 20 bins.

    Parameters:
    - data (pd.DataFrame): The input data to be standardized and plotted. It is expected to have
                           columns named 'Feature1', 'Feature2', 'Feature3', 'Feature4', and 'Feature5'.
                           If there are additional data columns, they are ignored.


    Returns:
    - standardized_data (pd.DataFrame): The standardized data.
    - axes_list (list): A list of matplotlib Axes objects representing the histograms for each feature.

    Requirements:
    - pandas
    - matplotlib.pyplot
    - sklearn.preprocessing.StandardScaler
    
    Example:
    >>> data = pd.DataFrame({
    ...     'Feature1': [0.5, 0.6, 0.7, 0.8, 0.9],
    ...     'Feature2': [0.1, 0.2, 0.3, 0.4, 0.5],
    ...     'Feature3': [0.9, 0.8, 0.7, 0.6, 0.5],
    ...     'Feature4': [0.5, 0.4, 0.3, 0.2, 0.1],
    ...     'Feature5': [0.1, 0.3, 0.5, 0.7, 0.9]
    ... })
    >>> standardized_data, axes_list = task_func(data)
    >>> type(standardized_data)
    <class 'pandas.core.frame.DataFrame'>
    >>> axes_list
    [<Axes: title={'center': 'Histogram of Feature1'}>, <Axes: title={'center': 'Histogram of Feature2'}>, <Axes: title={'center': 'Histogram of Feature3'}>, <Axes: title={'center': 'Histogram of Feature4'}>, <Axes: title={'center': 'Histogram of Feature5'}>]
    >>> type(axes_list[0])
    <class 'matplotlib.axes._axes.Axes'>
    """

instruct prompt

This function takes a pandas DataFrame and standardizes its features using sklearn's StandardScaler, which standardizes features by removing the mean and scaling to unit variance. After standardization, it draws a histogram for each feature with 20 bins.
The function should output with:
    standardized_data (pd.DataFrame): The standardized data.
    axes_list (list): A list of matplotlib Axes objects representing the histograms for each feature.
You should write self-contained code starting with:

Code

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
def task_func(data: pd.DataFrame) -> (pd.DataFrame, list):

code prompt

Code

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
def task_func(data: pd.DataFrame) -> (pd.DataFrame, list):

entry point

task_func

libs

  • pandas
  • matplotlib
  • sklearn

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Source and history

task_id
764c7ab1-a29d-5ff7-ae7e-e08b08b2870c
task_key
default--v0~2e1~2e0~5fhf--764c7ab1-a29d-5ff7-ae7e-e08b08b2870c
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import