BigCodeBench / BigCodeBench v0.1.0_hf 8abf9b67-9147-5644-902e-1edf5a92fea2

complete prompt

import pandas as pd
import seaborn as sns

# Constants
COLUMNS = ['col1', 'col2', 'col3']

def task_func(data):
    """
    You are given a list of elements. Each element of the list is a list of 3 values. Use this list of elements to build a dataframe with 3 columns 'col1', 'col2' and 'col3' and create a distribution of chart of the different values of "col3" grouped by "col1" and "col2" using seaborn.

    The function's logic is as follows:
    1. Build a pandas DataFrame by using list of elements. Make sure to name the columns as 'col1', 'col2' and 'col3', the constant COLUMNS is provided for this purpose.
    2. Create a new dataframe by grouping the values in the column 'col3' by ['col1', 'col2'].
    3. Reset the index of the newly created dataframe. This dataframe is the first element of the output tuple.
    4. Create a distribution plot of the 'col3' column of the previous dataframe using seaborn. This plot is the second and last element of the output tuple.
        - The xlabel (label for the x-axis) is set to the 'col3'.

    Parameters:
    data (list): The DataFrame to be visualized.

    Returns:
    tuple:
        pandas.DataFrame: The DataFrame of the analyzed data.
        plt.Axes: The seaborn plot object.

    Requirements:
    - pandas
    - seaborn

    Example:
    >>> data = [[1, 1, 1], [1, 1, 1], [1, 1, 2], [1, 2, 3], [1, 2, 3], [1, 2, 3], [2, 1, 1], [2, 1, 2], [2, 1, 3], [2, 2, 3], [2, 2, 3], [2, 2, 3]]
    >>> analyzed_df, plot = task_func(data)
    >>> print(analyzed_df)
       col1  col2  col3
    0     1     1     2
    1     1     2     1
    2     2     1     3
    3     2     2     1
    """

instruct prompt

You are given a list of elements. Each element of the list is a list of 3 values. Use this list of elements to build a dataframe with 3 columns 'col1', 'col2' and 'col3' and create a distribution of chart of the different values of "col3" grouped by "col1" and "col2" using seaborn. The function's logic is as follows: 1. Build a pandas DataFrame by using list of elements. Make sure to name the columns as 'col1', 'col2' and 'col3', the constant COLUMNS is provided for this purpose. 2. Create a new dataframe by grouping the values in the column 'col3' by ['col1', 'col2']. 3. Reset the index of the newly created dataframe. This dataframe is the first element of the output tuple. 4. Create a distribution plot of the 'col3' column of the previous dataframe using seaborn. This plot is the second and last element of the output tuple. - The xlabel (label for the x-axis) is set to the 'col3'.
The function should output with:
    tuple:
    pandas.DataFrame: The DataFrame of the analyzed data.
    plt.Axes: The seaborn plot object.
You should write self-contained code starting with:

Code

import pandas as pd
import seaborn as sns
# Constants
COLUMNS = ['col1', 'col2', 'col3']
def task_func(data):

code prompt

Code

import pandas as pd
import seaborn as sns
# Constants
COLUMNS = ['col1', 'col2', 'col3']
def task_func(data):

entry point

task_func

libs

• pandas
• seaborn