简化大数据处理脚本

def load_data(file_path):
    # 加载数据
    pass

def clean_data(data):
    # 清洗数据
    pass

def transform_data(data):
    # 转换数据
    pass

def save_data(data, output_path):
    # 保存数据
    pass

def main():
    data = load_data('input.csv')
    cleaned_data = clean_data(data)
    transformed_data = transform_data(cleaned_data)
    save_data(transformed_data, 'output.csv')

if __name__ == "__main__":
    main()

import configparser

config = configparser.ConfigParser()
config.read('config.ini')

input_file = config['DEFAULT']['input_file']
output_file = config['DEFAULT']['output_file']

import concurrent.futures

def process_chunk(chunk):
    # 处理数据块
    pass

with concurrent.futures.ThreadPoolExecutor() as executor:
    futures = [executor.submit(process_chunk, chunk) for chunk in data_chunks]
    concurrent.futures.wait(futures)

import pandas as pd

data = pd.read_csv('input.csv')
data.dropna(inplace=True)
data.to_csv('output.csv', index=False)

def clean_data(data):
    """
    清洗数据，去除缺失值和异常值
    :param data: 输入数据
    :return: 清洗后的数据
    """
    data.dropna(inplace=True)
    data = data[(data['value'] > 0) & (data['value'] < 100)]
    return data

import unittest

class TestDataProcessing(unittest.TestCase):
    def test_load_data(self):
        data = load_data('test_input.csv')
        self.assertEqual(len(data), 100)

if __name__ == "__main__":
    unittest.main()