BigCodeBench / BigCodeBench v0.1.0_hf a70129c5-da1c-5a67-bb69-c07eab3c593b

complete prompt

import pandas as pd
from sklearn.preprocessing import MinMaxScaler


def task_func(data_dict, data_keys):
    """
    Normalize data specified by keys in a dictionary using MinMax scaling and plot the results. This function is
    useful for preprocessing data for machine learning models where data scaling can impact performance.

    Parameters:
    data_dict (dict): A dictionary where keys map to lists of numeric values.
    data_keys (list): Keys within the dictionary whose corresponding values are to be normalized.

    Returns:
    tuple: A tuple containing a DataFrame of normalized values and a matplotlib Axes object representing a plot of the
    normalized data.

    Requirements:
    - pandas
    - sklearn

    Raises:
    ValueError: If no keys in `data_keys` are found in `data_dict`.

    Example:
    >>> data_dict = {'A': [1, 2, 3], 'B': [4, 5, 6]}
    >>> data_keys = ['A', 'B']
    >>> normalized_df, ax = task_func(data_dict, data_keys)
    >>> print(normalized_df.to_string(index=False))
      A   B
    0.0 0.0
    0.5 0.5
    1.0 1.0
    """

instruct prompt

Normalize data specified by keys in a dictionary using MinMax scaling and plot the results. This function is useful for preprocessing data for machine learning models where data scaling can impact performance.
The function should raise the exception for: ValueError: If no keys in `data_keys` are found in `data_dict`.
The function should output with:
    tuple: A tuple containing a DataFrame of normalized values and a matplotlib Axes object representing a plot of the
    normalized data.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.preprocessing import MinMaxScaler
def task_func(data_dict, data_keys):

code prompt

Code

import pandas as pd
from sklearn.preprocessing import MinMaxScaler
def task_func(data_dict, data_keys):

entry point

task_func

libs

• pandas
• sklearn