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BigCodeBench / BigCodeBench v0.1.0_hf 414943b0-e237-5079-bd5d-2177351db1ff

Problem

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

complete prompt

import json
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from collections import defaultdict


def task_func(input_file: str) -> plt.Axes:
    """
    Read a list of dictionaries from a JSON file, calculate the results (mean and median for each key)
    via numpy, convert the input data into a pandas DataFrame with the keys as "X" and values as "Y"
    for visualization with a seaborn box plot, then return the results and box plot.

    Parameters:
    - input_file (str): The input JSON file name with absolute path.

    Returns:
    - results (dict): Dictionary where each key is a unique key from the original input, and each
                      value is a corresponding dict, with keys 'mean' and 'median' and the statistics
                      as values.
    - ax (plt.Axes): The box plot of aggregated 'Values for Each Key' in the input data.

    Requirements:
    - json
    - seaborn
    - matplotlib.pyplot
    - pandas
    - numpy
    - collections.defaultdict

    Example:
    >>> results, ax = task_func("/path/to/data.json")
    >>> ax
    <class 'matplotlib.axes._axes.Axes'>
    >>> results
    {'a': {'mean': 3.0, 'median': 3.0}, 'b': {'mean': 2.0, 'median': 3.0}}
    """

instruct prompt

Read a list of dictionaries from a JSON file, calculate the results (mean and median for each key) via numpy, convert the input data into a pandas DataFrame with the keys as "X" and values as "Y" for visualization with a seaborn box plot, then return the results and box plot.
The function should output with:
    results (dict): Dictionary where each key is a unique key from the original input, and each
    value is a corresponding dict, with keys 'mean' and 'median' and the statistics
    as values.
    ax (plt.Axes): The box plot of aggregated 'Values for Each Key' in the input data.
You should write self-contained code starting with:

Code

import json
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from collections import defaultdict
def task_func(input_file: str) -> plt.Axes:

code prompt

Code

import json
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from collections import defaultdict
def task_func(input_file: str) -> plt.Axes:

entry point

task_func

libs

  • pandas
  • collections
  • matplotlib
  • numpy
  • seaborn
  • json

Discussion

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Source and history

task_id
414943b0-e237-5079-bd5d-2177351db1ff
task_key
default--v0~2e1~2e0~5fhf--414943b0-e237-5079-bd5d-2177351db1ff
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import