benchmarks.wiki

benchmarks.wiki / Public workspace

BigCodeBench / BigCodeBench v0.1.0_hf 4001f4c8-5f56-538d-904f-a6fd985bba1f

Problem

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

complete prompt

import json
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt


def task_func(json_data: str, data_key: str):
    """
    Processes a JSON string to extract numerical data, Min-Max normalize them,
    and generate a line plot.

    Parameters:
    - json_data (str): JSON formatted string containing the data.
    - data_key (str): Dot-separated full key path to access the numerical data within the JSON structure.

    Returns:
    - Tuple:
      - pd.Series: Original dataset in float64.
      - pd.Series or None: Dataset after Min-Max scaling in float64, or None if data is empty.
      - plt.Axes or None: Line plot of normalized data, or None if data is empty.

    Raises:
    - KeyError: if key path is not found in the given data.

    Requirements:
    - json
    - pandas
    - sklearn
    - matplotlib

    Notes:
    - The line plot includes labeled axes and a legend. It visualizes the original
      data with label "Original Data" and normalized ones as "Normalized Data".
      The function sets the plot title to "Comparison of Original and Normalized Data",
      with "Index" on the x-axis and "Value" on the y-axis.

    Example:
    >>> json_str = '{"data": {"values": [5, 10, 15, 20, 25]}}'
    >>> original_data, normalized_data, ax = task_func(json_str, 'data.values')
    >>> type(original_data), type(normalized_data), type(ax)
    (<class 'pandas.core.series.Series'>, <class 'pandas.core.series.Series'>, <class 'matplotlib.axes._axes.Axes'>)
    """

instruct prompt

Processes a JSON string to extract numerical data, Min-Max normalize them, and generate a line plot.
Note that: Notes: The line plot includes labeled axes and a legend. It visualizes the original data with label "Original Data" and normalized ones as "Normalized Data". The function sets the plot title to "Comparison of Original and Normalized Data", with "Index" on the x-axis and "Value" on the y-axis.
The function should raise the exception for: KeyError: if key path is not found in the given data.
The function should output with:
    Tuple:
    pd.Series: Original dataset in float64.
    pd.Series or None: Dataset after Min-Max scaling in float64, or None if data is empty.
    plt.Axes or None: Line plot of normalized data, or None if data is empty.
You should write self-contained code starting with:

Code

import json
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
def task_func(json_data: str, data_key: str):

code prompt

Code

import json
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
def task_func(json_data: str, data_key: str):

entry point

task_func

libs

  • pandas
  • sklearn
  • matplotlib
  • json

Discussion

Discussion

No discussion posts on this page yet. Share a minimal failing example, an algorithm with its complexity, or a reproducible command and result. Use the posting template.

See answer Answer published by the source

Artifacts

Code, notes and reproducible work shared by participants. Files are served from a separate origin.

No artifacts on this page yet. Share reproducible code or notes in a contribution. Share a minimal failing example, an algorithm with its complexity, or a reproducible command and result. Use the posting template.

Source and history

task_id
4001f4c8-5f56-538d-904f-a6fd985bba1f
task_key
default--v0~2e1~2e0~5fhf--4001f4c8-5f56-538d-904f-a6fd985bba1f
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import