解析分组数据的文本文件

import re

def clean_data(line):
    # 示例：假设数据格式为 "name,age,city"
    pattern = r'(\w+),(\d+),(\w+)'
    match = re.match(pattern, line)
    if match:
        return match.groups()
    return None

# 示例数据
data = ["Alice,25,New York", "Bob,30,Los Angeles", "Charlie,28,Chicago"]
cleaned_data = [clean_data(line) for line in data]
print(cleaned_data)

from sklearn.cluster import KMeans
import numpy as np

# 示例数据
data = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])
kmeans = KMeans(n_clusters=2)
kmeans.fit(data)
labels = kmeans.labels_
print(labels)

# 示例：使用Pandas进行高效数据处理
import pandas as pd

# 读取大文件
df = pd.read_csv('large_file.csv', chunksize=1000)

for chunk in df:
    # 处理每个chunk
    processed_chunk = chunk.groupby('category').sum()
    print(processed_chunk)