获取数据帧行中n个最大绝对值的索引

import pandas as pd
import numpy as np

def get_n_largest_abs_indices(df, n):
    # 计算每行的绝对值
    abs_values = df.abs()
    # 获取每行最大绝对值的索引
    indices = abs_values.idxmax(axis=1)
    # 对索引按绝对值大小排序，并取前n个
    sorted_indices = indices.sort_values().head(n)
    return sorted_indices

# 示例数据
data = {
    'A': [1, -3, 5],
    'B': [-2, 4, -6],
    'C': [3, -5, 7]
}
df = pd.DataFrame(data)

# 获取每行中2个最大绝对值的索引
n = 2
result = get_n_largest_abs_indices(df, n)
print(result)

import dask.dataframe as dd

def get_n_largest_abs_indices_dask(df, n):
    abs_values = df.abs()
    indices = abs_values.idxmax(axis=1)
    sorted_indices = indices.compute().sort_values().head(n)
    return sorted_indices

# 将Pandas DataFrame转换为Dask DataFrame
ddf = dd.from_pandas(df, npartitions=2)

# 使用Dask获取每行中2个最大绝对值的索引
result_dask = get_n_largest_abs_indices_dask(ddf, n)
print(result_dask)