BigCodeBench / BigCodeBench v0.1.0_hf e3fa0e77-4193-51ac-9e97-e71a02fff873

complete prompt

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

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

def task_func(data):
    """
    You are given a list of elements. Each element is a list with the same length as COLUMNS, representing one row a dataframe df to create. Visualize the distribution of different values in a column "col3" of a pandas DataFrame df, grouped by "col1" and "col2," using a heatmap.

    Parameters:
    - data (list): A list of elements. Each element is a list with the same length as COLUMNS, representing one row of the dataframe to build.

    Returns:
    - tuple:
        pandas.DataFrame: The DataFrame of the analyzed data.
        plt.Axes: The heatmap visualization.

    Requirements:
    - pandas
    - seaborn
    - matplotlib

    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, ax = task_func(data)
    >>> print(analyzed_df)
    col2  1  2
    col1      
    1     2  1
    2     3  1
    """

instruct prompt

You are given a list of elements. Each element is a list with the same length as COLUMNS, representing one row a dataframe df to create. Visualize the distribution of different values in a column "col3" of a pandas DataFrame df, grouped by "col1" and "col2," using a heatmap.
The function should output with:
    tuple:
    pandas.DataFrame: The DataFrame of the analyzed data.
    plt.Axes: The heatmap visualization.
You should write self-contained code starting with:

Code

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

code prompt

Code

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

entry point

task_func

libs

• pandas
• matplotlib
• seaborn