为什么groupby这么快？

Question

这是对此问题的一个后续问题,其中jezrael使用pandas.DataFrame.groupby将列表创建速度提高几百倍.具体来说,让我们df成为一个大型数据帧

index = list(set(df.index))
list_df = [df.loc(x) for x in index]

和

list_df = [x for i,x in df.groupby(level=0, sort=False)]

产生相同的结果,后者比前者快200多倍,甚至忽略了列表创建步骤.为什么？

如果有人能让我理解为什么会有这么大的性能差异,我会很高兴的.提前致谢!

编辑:正如Alex Riley在评论中所建议的,我确认测试是在具有非唯一和非单调索引的数据框架上运行的.

Answer 1

因为您的数据帧没有在索引上排序,这意味着所有子集都必须使用慢矢量扫描完成,而快速算法如二进制搜索不能应用; 虽然groupby总是首先按变量对数据帧进行排序,但您可以通过编写对索引进行排序的简单算法来模拟此行为,然后对子集进行分类以验证:

def sort_subset(df):
    # sort index and find out the positions that separate groups
    df = df.sort_index()
    split_indices = np.flatnonzero(np.ediff1d(df.index, to_begin=1, to_end=1))
    list_df = []
    for i in range(len(split_indices)-1):
        start_index = split_indices[i]
        end_index = split_indices[i+1]
        list_df.append(df.iloc[start_index:end_index])
    return list_df

一些时间:

import pandas as pd
import numpy as np
?
nrow = 1000000
df = pd.DataFrame(np.random.randn(nrow), columns=['x'], index=np.random.randint(100, size=nrow))

index = list(set(df.index))
print('no of groups: ', len(index))
?
%timeit list_df_1 = [df.loc[x] for x in index]
#no of groups:  100
#13.6 s ± 228 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit list_df_2 = [x for i, x in df.groupby(level=0, sort=False)]
#54.8 ms ± 1.36 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

# Not as fast because my algorithm is not optimized at all but the same order of magnitude
%timeit list_df_3 = sort_subset(df)
#102 ms ± 3.53 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

list_df_1 = [df.loc[x] for x in index]
list_df_2 = [x for i, x in df.groupby(level=0, sort=False)]
list_df_3 = sort_subset(df)

比较结果:

all(list_df_3[i].eq(list_df_2[i]).all().iat[0] for i in range(len(list_df_2)))
# True

如果在子集化之前对索引进行排序,您会看到显着的加速:

def sort_subset_with_loc(df):
    df = df.sort_index()
    list_df_1 = [df.loc[x] for x in index]
    return list_df_1

%timeit sort_subset_with_loc(df)
# 25.4 ms ± 897 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)