如何有效地对数据帧进行倍增

import pandas as pd

# 示例数据帧
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# 倍增数据帧
doubled_df = pd.concat([df, df], ignore_index=True)

# 去重
unique_df = doubled_df.drop_duplicates()
print(unique_df)

import pandas as pd

# 示例数据帧
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# 分块倍增数据帧
chunk_size = 2
doubled_chunks = []

for i in range(0, len(df), chunk_size):
    chunk = df[i:i + chunk_size]
    doubled_chunk = pd.concat([chunk, chunk], ignore_index=True)
    doubled_chunks.append(doubled_chunk)

doubled_df = pd.concat(doubled_chunks, ignore_index=True)
print(doubled_df)

import pandas as pd
from multiprocessing import Pool

# 示例数据帧
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# 倍增数据帧的函数
def double_chunk(chunk):
    return pd.concat([chunk, chunk], ignore_index=True)

# 分块倍增数据帧
chunk_size = 2
chunks = [df[i:i + chunk_size] for i in range(0, len(df), chunk_size)]

# 并行处理
with Pool(processes=4) as pool:
    doubled_chunks = pool.map(double_chunk, chunks)

doubled_df = pd.concat(doubled_chunks, ignore_index=True)
print(doubled_df)