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BigCodeBench / BigCodeBench v0.1.0_hf 20edf78c-d745-5f81-bb41-95ee7338aad8

Problem

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

complete prompt

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

# Constants
LETTERS = list(string.ascii_lowercase)


def task_func(rows=1000, string_length=3):
    """
    Generate a dataframe of random strings and create a heatmap showing the correlation
    in the frequency of each letter in these strings.

    This function generates a specified number of random strings, each of a given length,
    and calculates the frequency of each letter in these strings. A heatmap of the 
    correlation matrix is then displayed, showing the co-occurrence frequencies of different 
    letters within these strings.

    If the number of rows specified is zero, the function will print a message indicating
    that no data is available to generate the heatmap and will return None. Otherwise, 
    it processes the DataFrame to convert the generated strings into a one-hot encoded format
    and then sums up these encodings to calculate the frequency of each letter.

    Parameters:
    - rows (int, optional): Number of random strings to generate. Must be non-negative. 
      Default is 1000. If set to 0, the function returns None after printing a message.
    - string_length (int, optional): Length of each random string. Must be non-negative. 
      Default is 3. A value of 0 results in the generation of empty strings.

    Returns:
    - matplotlib.axes._axes.Axes or None: A seaborn heatmap plot object if 
      data is generated; otherwise, None.

    Requirements:
    - random
    - string
    - pandas
    - seaborn
    - matplotlib

    Note
    - If no strings are generated (e.g., rows = 0), the 
       DataFrame will be empty. In this case, the function prints a message "No data to generate heatmap." and returns None.
    - If the DataFrame is not empty, each string is split into its 
       constituent letters, converted into one-hot encoded format, and then the frequency 
       of each letter is calculated by summing these encodings.
       
    Example:
    >>> ax = task_func(1000, 3)
    >>> ax.get_xlim()
    (0.0, 26.0)
    """

instruct prompt

Generate a dataframe of random strings and create a heatmap showing the correlation in the frequency of each letter in these strings. This function generates a specified number of random strings, each of a given length, and calculates the frequency of each letter in these strings. A heatmap of the correlation matrix is then displayed, showing the co-occurrence frequencies of different letters within these strings. If the number of rows specified is zero, the function will print a message indicating that no data is available to generate the heatmap and will return None. Otherwise, it processes the DataFrame to convert the generated strings into a one-hot encoded format and then sums up these encodings to calculate the frequency of each letter. Note - If no strings are generated (e.g., rows = 0), the DataFrame will be empty. In this case, the function prints a message "No data to generate heatmap." and returns None. - If the DataFrame is not empty, each string is split into its constituent letters, converted into one-hot encoded format, and then the frequency of each letter is calculated by summing these encodings.
The function should output with:
    matplotlib.axes._axes.Axes or None: A seaborn heatmap plot object if
    data is generated; otherwise, None.
You should write self-contained code starting with:

Code

import matplotlib.pyplot as plt
import random
import string
import pandas as pd
import seaborn as sns
# Constants
LETTERS = list(string.ascii_lowercase)
def task_func(rows=1000, string_length=3):

code prompt

Code

import matplotlib.pyplot as plt
import random
import string
import pandas as pd
import seaborn as sns
# Constants
LETTERS = list(string.ascii_lowercase)
def task_func(rows=1000, string_length=3):

entry point

task_func

libs

  • pandas
  • matplotlib
  • random
  • string
  • seaborn

Discussion

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Source and history

task_id
20edf78c-d745-5f81-bb41-95ee7338aad8
task_key
default--v0~2e1~2e0~5fhf--20edf78c-d745-5f81-bb41-95ee7338aad8
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import