熊猫统计函数和Boost::Accumulator之间的差异

我得到了不同的统计计算结果 pandas 和 boost::accumulators 我不确定为什么。

下面我有一个简单的例子,用熊猫计算一些收益的均值和方差。

import pandas

vals = [ 1, 1, 2, 1, 3, 2, 3, 4, 6, 3, 2, 1 ]
rets = pandas.Series(vals).pct_change()

print(f'count:    {len(rets)}')
print(f'mean:     {rets.mean()}')
print(f'variance: {rets.var()}')

其结果是:

count:    12
mean:     0.19696969696969696
variance: 0.6156565656565657

我正在做C++中的等价用法 增压::蓄能器 用于统计计算

#include <iostream>
#include <iomanip>
#include <cmath>
#include <boost/accumulators/accumulators.hpp>
#include <boost/accumulators/statistics/stats.hpp>
#include <boost/accumulators/statistics/count.hpp>
#include <boost/accumulators/statistics/mean.hpp>
#include <boost/accumulators/statistics/variance.hpp>

namespace acc = boost::accumulators;

int main()
{
    acc::accumulator_set<double, acc::stats<acc::tag::count,
                                            acc::tag::mean,
                                            acc::tag::variance>> stats;

    double prev = NAN;
    for (double val : { 1, 1, 2, 1, 3, 2, 3, 4, 6, 3, 2, 1 })
    {
        const double ret = (val - prev) / prev;

        stats(std::isnan(ret) ? 0 : ret);

        prev = val;
    }

    std::cout << std::setprecision(16)
              << "count:    " << acc::count(stats)    << '\n'
              << "mean:     " << acc::mean(stats)     << '\n'
              << "variance: " << acc::variance(stats) << '\n';

    return 0;
}

其结果是:

count:    12
mean:     0.1805555555555556
variance: 0.5160108024691359

• 为什么大熊猫和Boost::Accumulators之间的平均值和方差不同?
• 我需要做什么才能从Boost::Accumulators得到熊猫的结果?