Bigcodebench

BigCodeBench

Write a Python function that satisfies the given instructions and passes its hidden unit tests. Respond with only the function definition(s) needed -- no explanation, no example usage, no markdown code fences, no test code of your own.

General Strategies

Input Validation and Error Handling

• Check for invalid inputs: Before performing operations, validate that inputs are of the expected type and format. Handle edge cases such as empty lists, zero values, or invalid file paths.
• Raise specific exceptions: When errors occur due to invalid inputs or unexpected conditions, raise appropriate exceptions (e.g., ValueError, FileNotFoundError, statistics.StatisticsError) with informative messages.
• Handle subprocess errors: When using subprocess, use check=True or explicitly check the return code and capture stderr to handle command execution failures gracefully.

Data Manipulation and Transformation

• Use appropriate libraries: Leverage libraries like pandas, numpy, collections, itertools, random, statistics, scipy, and sklearn for efficient data manipulation, statistical calculations, and machine learning tasks.
• Handle missing values: Before performing calculations or visualizations, address missing values (NaNs) by imputing them (e.g., with the column's mean) or by dropping rows/columns as appropriate for the task.
• Type conversion: Ensure data types are correct before performing operations. For example, convert string representations of numbers or dictionaries to their actual types using int(), float(), ast.literal_eval(), or json.loads().
• Data normalization and scaling: Use StandardScaler or MinMaxScaler from sklearn.preprocessing when data requires normalization or scaling for certain algorithms or visualizations.

File Operations

• Check file/directory existence: Before reading from or writing to files/directories, verify their existence using os.path.exists(). Create directories if they don't exist using os.makedirs().
• Safe file handling: Use with open(...) for file operations to ensure files are properly closed, even if errors occur.
• Archiving and compression: Utilize libraries like zipfile and shutil for creating archives and tarfile or subprocess with tar for creating compressed archives.

Visualization

• Clear plot labeling: Ensure all plots have descriptive titles, x-axis labels, and y-axis labels.
• Appropriate plot types: Choose visualization types (e.g., bar plot, histogram, heatmap, scatter plot, line plot, box plot, KDE plot) that best represent the data and the task's requirements.
• Handle empty data for plotting: If a plot cannot be generated due to empty or insufficient data, return None or an appropriate indicator as specified by the task.
• Figure and Axes management: When creating plots, manage Figure and Axes objects correctly, especially when returning them or saving plots to files.

Algorithmic Strategies

• Iterate and aggregate: For tasks involving processing collections of items (lists, dictionaries, files), iterate through them and aggregate results using appropriate methods (e.g., sum(), mean(), Counter(), reduce()).
• Conditional logic: Implement conditional logic (if/else) to handle different scenarios, such as varying input lengths, specific data patterns, or error conditions.
• Regular expressions: Use re for pattern matching and text manipulation, especially when dealing with strings, file names, or specific text formats.

Code Structure and Readability

• Import necessary modules: Ensure all required modules are imported at the beginning of the function.
• Use meaningful variable names: Choose descriptive names for variables to improve code clarity.
• Add comments: Include comments to explain complex logic or non-obvious steps.
• Follow function signature: Adhere to the provided function signature, including default arguments and type hints.
• Return expected types: Ensure the function returns values of the types specified in the task description.