butterlyn / calculator_app Goto Github PK

View Code? Open in Web Editor NEW

0.0 0.0 0.0 5 KB

testing sweepai

License: MIT License

Python 100.00%

calculator_app's People

Contributors

Watchers

calculator_app's Issues

sweep: create apply_function_to_dataframe.py

Updated Program Specification

Objective

Create a Python script that takes a function and a Pandas DataFrame as inputs, applies the function to each row of the DataFrame using multiprocessing, and maps specific columns to the function's arguments using DataFrame column names as function argument names. The results should be returned as a Pandas Series or DataFrame. Additionally, write an additional function within the script that horizontally appends the two DataFrames (or DataFrame and Series) and outputs them as a CSV file. Handle errors by leaving the respective cell blank, outputting a warning, and using logging for console and log.log file outputs. Assume the program runs on a Windows OS.

Features

Apply the provided function to each row of the input DataFrame using multiprocessing, mapping DataFrame columns to function arguments.
Handle errors by leaving the respective cell blank and outputting a warning.
Log errors to both the console and a log.log file.
Append the results to the input DataFrame horizontally.
Output the combined DataFrame as a CSV file.

Inputs

num_workers: An optional integer value representing the number of parallel workers to use. Default value should be the number of available CPU cores.

Core Classes, Functions, and Methods

apply_function_to_dataframe(dataframe: pd.DataFrame, func: Callable, num_workers: Optional[int] = None, **func_args) -> Union[pd.Series, pd.DataFrame]:
- Apply the provided function to the input DataFrame using multiprocessing, mapping DataFrame columns to function arguments. Return the results as a Pandas Series or DataFrame.

Implementation Notes

Update the apply_function_to_dataframe to use multiprocessing. You can use the concurrent.futures module to execute the function in parallel. Specifically, use the ProcessPoolExecutor for parallel processing on a Windows OS.

Here's the updated outline of how the apply_function_to_dataframe function should be implemented:

import pandas as pd
import numpy as np
import logging
from typing import Callable, Union, Optional
from concurrent.futures import ProcessPoolExecutor

# Configure logging settings
logging.basicConfig(filename='log.log', level=logging.WARNING, format='%(asctime)s %(levelname)s: %(message)s')

def apply_function_to_dataframe(dataframe: pd.DataFrame, func: Callable, num_workers: Optional[int] = None, **func_args) -> Union[pd.Series, pd.DataFrame]:
    """
    Apply the provided function to the input DataFrame using multiprocessing, mapping DataFrame columns to function arguments.

    Args:
        dataframe (pd.DataFrame): The input dataframe.
        func (Callable): The function to apply to each row of the dataframe.
        num_workers (Optional[int], optional): The number of parallel workers to use. Defaults to None.
        **func_args: Additional keyword arguments for the function.

    Returns:
        Union[pd.Series, pd.DataFrame]: A Pandas Series or DataFrame containing the results of applying the function to each row of the input dataframe.
    """
    def apply_helper(row):
        try:
            return func(**row[func_args].to_dict())
        except Exception as e:
            logging.warning(f"Error applying function to row: {e}")
            return np.nan

    with ProcessPoolExecutor(max_workers=num_workers) as executor:
        results = list(executor.map(apply_helper, [row for _, row in dataframe.iterrows()]))

    return pd.Series(results)

The rest of the implementation remains the same. This updated implementation should meet the new requirements of the provided specification.

sweep: Create script apply_funtion_to_dataframe.py

Updated Program Specification

Objective

Features

Apply the provided function to each row of the input DataFrame using multiprocessing, mapping DataFrame columns to function arguments.
Handle errors by leaving the respective cell blank and outputting a warning.
Log errors to both the console and a log.log file.
Append the results to the input DataFrame horizontally.
Output the combined DataFrame as a CSV file.

Inputs

num_workers: An optional integer value representing the number of parallel workers to use. Default value should be the number of available CPU cores.

Core Classes, Functions, and Methods

apply_function_to_dataframe(dataframe: pd.DataFrame, func: Callable, num_workers: Optional[int] = None, **func_args) -> Union[pd.Series, pd.DataFrame]:
- Apply the provided function to the input DataFrame using multiprocessing, mapping DataFrame columns to function arguments. Return the results as a Pandas Series or DataFrame.

Implementation Notes

Update the apply_function_to_dataframe to use multiprocessing. You can use the concurrent.futures module to execute the function in parallel. Specifically, use the ProcessPoolExecutor for parallel processing on a Windows OS.

Here's the updated outline of how the apply_function_to_dataframe function should be implemented:

import pandas as pd
import numpy as np
import logging
from typing import Callable, Union, Optional
from concurrent.futures import ProcessPoolExecutor

# Configure logging settings
logging.basicConfig(filename='log.log', level=logging.WARNING, format='%(asctime)s %(levelname)s: %(message)s')

def apply_function_to_dataframe(dataframe: pd.DataFrame, func: Callable, num_workers: Optional[int] = None, **func_args) -> Union[pd.Series, pd.DataFrame]:
    """
    Apply the provided function to the input DataFrame using multiprocessing, mapping DataFrame columns to function arguments.

    Args:
        dataframe (pd.DataFrame): The input dataframe.
        func (Callable): The function to apply to each row of the dataframe.
        num_workers (Optional[int], optional): The number of parallel workers to use. Defaults to None.
        **func_args: Additional keyword arguments for the function.

    Returns:
        Union[pd.Series, pd.DataFrame]: A Pandas Series or DataFrame containing the results of applying the function to each row of the input dataframe.
    """
    def apply_helper(row):
        try:
            return func(**row[func_args].to_dict())
        except Exception as e:
            logging.warning(f"Error applying function to row: {e}")
            return np.nan

    with ProcessPoolExecutor(max_workers=num_workers) as executor:
        results = list(executor.map(apply_helper, [row for _, row in dataframe.iterrows()]))

    return pd.Series(results)

The rest of the implementation remains the same. This updated implementation should meet the new requirements of the provided specification.

butterlyn / calculator_app Goto Github PK

calculator_app's People

Contributors

Watchers

calculator_app's Issues

Updated Program Specification

Objective

Features

Inputs

Core Classes, Functions, and Methods

Implementation Notes

Updated Program Specification

Objective

Features

Inputs

Core Classes, Functions, and Methods

Implementation Notes

Recommend Projects

Recommend Topics

Recommend Org