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BigCodeBench / BigCodeBench v0.1.0_hf a645cbba-f7bf-59c1-912c-b9ed5e229e7a

Problem

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

complete prompt

import pandas as pd
from sklearn.preprocessing import MinMaxScaler


def task_func(list_of_pairs):
    """
    Create a Pandas DataFrame from a list of pairs and normalize the data using MinMaxScaler.
    
    Parameters:
    list_of_pairs (list): A list of tuples, where the first element is the category and 
                          the second element is the value.
    
    Returns:
    DataFrame:  A pandas DataFrame containing the columns 'Category' and 'Value'.
                Category contains the the first elements of each tuple.
                Value contains the normalized values of each tuple.

    Raises:
        Exception: If the input array is empty.
        ValueError: If Values are not numeric.
    
    Requirements:
    - pandas
    - sklearn.preprocessing.MinMaxScaler
    
    Example:
    >>> list_of_pairs = [('Fruits', 5), ('Vegetables', 9), ('Dairy', -1), ('Bakery', -2), ('Meat', 4)]
    >>> df = task_func(list_of_pairs)
    >>> print(df)
         Category     Value
    0      Fruits  0.636364
    1  Vegetables  1.000000
    2       Dairy  0.090909
    3      Bakery  0.000000
    4        Meat  0.545455
    >>> list_of_pairs = [('car', 3.2), ('bike', 0), ('train', -1), ('plane', -6.2), ('ship', 1234)]
    >>> df = task_func(list_of_pairs)
    >>> print(df)
      Category     Value
    0      car  0.007579
    1     bike  0.004999
    2    train  0.004193
    3    plane  0.000000
    4     ship  1.000000
    """

instruct prompt

Create a Pandas DataFrame from a list of pairs and normalize the data using MinMaxScaler.
The function should raise the exception for: Exception: If the input array is empty. ValueError: If Values are not numeric.
The function should output with:
    DataFrame:  A pandas DataFrame containing the columns 'Category' and 'Value'.
    Category contains the the first elements of each tuple.
    Value contains the normalized values of each tuple.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.preprocessing import MinMaxScaler
def task_func(list_of_pairs):

code prompt

Code

import pandas as pd
from sklearn.preprocessing import MinMaxScaler
def task_func(list_of_pairs):

entry point

task_func

libs

  • pandas
  • sklearn

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Source and history

task_id
a645cbba-f7bf-59c1-912c-b9ed5e229e7a
task_key
default--v0~2e1~2e0~5fhf--a645cbba-f7bf-59c1-912c-b9ed5e229e7a
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import