benchmarks.wiki

benchmarks.wiki / Public workspace

BigCodeBench / BigCodeBench v0.1.0_hf d5c6801b-2ba3-5377-9802-5c405ea0c8ac

Problem

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

complete prompt

import numpy as np
from scipy import stats

# Constants
FEATURES = ['feature1', 'feature2', 'feature3', 'feature4', 'feature5']

def task_func(df, dct):
    """
    This function calculates and returns the mean, median, mode, and variance for specified features in a DataFrame. 
    It replaces certain values in the DataFrame based on a provided dictionary mapping before performing the calculations.
    
    Parameters:
    df (DataFrame): The input DataFrame.
    dct (dict): A dictionary for replacing values in df.
    
    Returns:
    dict: A dictionary containing statistics (mean, median, mode, variance) for each feature defined in the 'FEATURES' constant.
    
    Requirements:
    - numpy
    - scipy.stats

    Note:
    - The function would return "Invalid input" string if the input is invalid (e.g., does not contain the required 'feature1' key) or if there is an error in the calculation.
    
    Example:
    >>> df = pd.DataFrame({'feature1': [1, 2, 3, 4, 5], 'feature2': [5, 4, 3, 2, 1], 'feature3': [2, 2, 2, 2, 2], 'feature4': [1, 1, 3, 3, 5], 'feature5': [0, 1, 1, 1, 1]})
    >>> dct = {}
    >>> task_func(df, dct)
    {'feature1': {'mean': 3.0, 'median': 3.0, 'mode': 1, 'variance': 2.0}, 'feature2': {'mean': 3.0, 'median': 3.0, 'mode': 1, 'variance': 2.0}, 'feature3': {'mean': 2.0, 'median': 2.0, 'mode': 2, 'variance': 0.0}, 'feature4': {'mean': 2.6, 'median': 3.0, 'mode': 1, 'variance': 2.24}, 'feature5': {'mean': 0.8, 'median': 1.0, 'mode': 1, 'variance': 0.16000000000000006}}
    """

instruct prompt

This function calculates and returns the mean, median, mode, and variance for specified features in a DataFrame. It replaces certain values in the DataFrame based on a provided dictionary mapping before performing the calculations.
Note that: The function would return "Invalid input" string if the input is invalid (e.g., does not contain the required 'feature1' key) or if there is an error in the calculation.
The function should output with:
    dict: A dictionary containing statistics (mean, median, mode, variance) for each feature defined in the 'FEATURES' constant.
You should write self-contained code starting with:

Code

import numpy as np
from scipy import stats
# Constants
FEATURES = ['feature1', 'feature2', 'feature3', 'feature4', 'feature5']
def task_func(df, dct):

code prompt

Code

import numpy as np
from scipy import stats
# Constants
FEATURES = ['feature1', 'feature2', 'feature3', 'feature4', 'feature5']
def task_func(df, dct):

entry point

task_func

libs

  • numpy
  • scipy

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
d5c6801b-2ba3-5377-9802-5c405ea0c8ac
task_key
default--v0~2e1~2e0~5fhf--d5c6801b-2ba3-5377-9802-5c405ea0c8ac
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import