Pandas应用函数速度慢

import pandas as pd

# 示例数据
df = pd.DataFrame({'A': range(1000000)})

# 使用向量化操作
df['A'] = df['A'].apply(lambda x: x * 2)

import pandas as pd
from numba import njit

# 示例数据
df = pd.DataFrame({'A': range(1000000)})

# 使用numba加速
@njit
def multiply_by_two(x):
    return x * 2

df['A'] = df['A'].apply(multiply_by_two)

import pandas as pd
import swifter

# 示例数据
df = pd.DataFrame({'A': range(1000000)})

# 使用swifter加速
df['A'] = df['A'].swifter.apply(lambda x: x * 2)

import pandas as pd

# 示例数据
df = pd.DataFrame({'A': range(1000000)})

# 分块处理
chunk_size = 100000
results = []

for chunk in pd.read_csv('large_dataset.csv', chunksize=chunk_size):
    chunk['A'] = chunk['A'].apply(lambda x: x * 2)
    results.append(chunk)

final_df = pd.concat(results)

import dask.dataframe as dd

# 示例数据
df = dd.from_pandas(pd.DataFrame({'A': range(1000000)}), npartitions=10)

# 使用Dask加速
df['A'] = df['A'].apply(lambda x: x * 2, meta=('A', 'i8'))

# 计算结果
result = df.compute()