t测试数据框内的组是否具有唯一id

Question

我有下面的数据框，我正在对每个ID在一个月内的所有工作日和周末的所有日子之间执行t检验

> +-----+------------+-----------+---------+-----------+ | id  | usage_day  | dow       | tow     | daily_avg |
> +-----+------------+-----------+---------+-----------+ | c96 | 01/09/2020 | Tuesday   | week    | 393.07    |
> +-----+------------+-----------+---------+-----------+ | c96 | 02/09/2020 | Wednesday | week    | 10.38     |
> +-----+------------+-----------+---------+-----------+ | c96 | 03/09/2020 | Thursday  | week    | 429.35    |
> +-----+------------+-----------+---------+-----------+ | c96 | 04/09/2020 | Friday    | week    | 156.20    |
> +-----+------------+-----------+---------+-----------+ | c96 | 05/09/2020 | Saturday  | weekend | 346.22    |
> +-----+------------+-----------+---------+-----------+ | c96 | 06/09/2020 | Sunday    | weekend | 106.53    |
> +-----+------------+-----------+---------+-----------+ | c96 | 08/09/2020 | Tuesday   | week    | 194.74    |
> +-----+------------+-----------+---------+-----------+ | c96 | 10/09/2020 | Thursday  | week    | 66.30     |
> +-----+------------+-----------+---------+-----------+ | c96 | 17/09/2020 | Thursday  | week    | 163.84    |
> +-----+------------+-----------+---------+-----------+ | c96 | 18/09/2020 | Friday    | week    | 261.81    |
> +-----+------------+-----------+---------+-----------+ | c96 | 19/09/2020 | Saturday  | weekend | 410.30    |
> +-----+------------+-----------+---------+-----------+ | c96 | 20/09/2020 | Sunday    | weekend | 266.28    |
> +-----+------------+-----------+---------+-----------+ | c96 | 23/09/2020 | Wednesday | week    | 346.18    |
> +-----+------------+-----------+---------+-----------+ | c96 | 24/09/2020 | Thursday  | week    | 20.67     |
> +-----+------------+-----------+---------+-----------+ | c96 | 25/09/2020 | Friday    | week    | 222.23    |
> +-----+------------+-----------+---------+-----------+ | c96 | 26/09/2020 | Saturday  | weekend | 449.84    |
> +-----+------------+-----------+---------+-----------+ | c96 | 27/09/2020 | Sunday    | weekend | 438.47    |
> +-----+------------+-----------+---------+-----------+ | c96 | 28/09/2020 | Monday    | week    | 10.44     |
> +-----+------------+-----------+---------+-----------+ | c96 | 29/09/2020 | Tuesday   | week    | 293.59    |
> +-----+------------+-----------+---------+-----------+ | c96 | 30/09/2020 | Wednesday | week    | 194.49    |
> +-----+------------+-----------+---------+-----------+

我的脚本如下，但不幸的是，它太慢了，不是熊猫做事的方式。 我怎样才能做得更有效

    from scipy.stats import ttest_ind, ttest_ind_from_stats

    p_val = []
    stat_flag = []
    all_ids = df.id.unique()
    alpha = 0.05
    print(len(all_ids))
    for id in all_ids:
        t = df[df.id ==  id]
        group1 = t[t.tow == 'week']
        group2 = t[t.tow == 'weekend']
        t, p_value_ttest = ttest_ind(group1.daily_avg, group2.daily_avg, equal_var=False)
        if p_value_ttest < alpha:
           p_val.append(p_value_ttest)
           stat_flag.append(1)
        else: 
           p_val.append(p_value_ttest)
           stat_flag.append(0)

p-val给出每个id的p值

Answer 1

数据集

根据您提供的数据集：

import io
from scipy import stats
import pandas as pd

s = """id|usage_day|dow|tow|daily_avg
c96|01/09/2020|Tuesday|week|393.07
c96|02/09/2020|Wednesday|week|10.38
c96|03/09/2020|Thursday|week|429.35
c96|04/09/2020|Friday|week|156.20
c96|05/09/2020|Saturday|weekend|346.22
c96|06/09/2020|Sunday|weekend|106.53
c96|08/09/2020|Tuesday|week|194.74
c96|10/09/2020|Thursday|week|66.30
c96|17/09/2020|Thursday|week|163.84
c96|18/09/2020|Friday|week|261.81
c96|19/09/2020|Saturday|weekend|410.30
c96|20/09/2020|Sunday|weekend|266.28
c96|23/09/2020|Wednesday|week|346.18
c96|24/09/2020|Thursday|week|20.67
c96|25/09/2020|Friday|week|222.23
c96|26/09/2020|Saturday|weekend|449.84
c96|27/09/2020|Sunday|weekend|438.47
c96|28/09/2020|Monday|week|10.44
c96|29/09/2020|Tuesday|week|293.59
c96|30/09/2020|Wednesday|week|194.49"""
df = pd.read_csv(io.StringIO(s), sep='|')

为了groupby清晰起见，我添加了一个具有类似数据的新id：

df2 = df.copy()
df2['id'] = 'c97'
df = pd.concat([df, df2])

MCVE

您不必求助于任何显式循环，而是利用^{}方法，该方法对帧进行操作，也可与^{}一起使用

为此，我们定义了一个函数，该函数在数据帧上执行所需的测试（groupby将为对应于分组键组合的每个子数据帧调用此方法）：

def ttest(x):
    g = x.groupby('tow').agg({'daily_avg': list})
    r = stats.ttest_ind(g.loc['week', 'daily_avg'], g.loc['weekend', 'daily_avg'], equal_var=False)
    s = {k: getattr(r, k) for k in r._fields}
    return pd.Series(s)

然后，在groupby调用之后链接apply就足够了：

T = df.groupby('id').apply(ttest)

结果是：

     statistic    pvalue
id                      
c96  -2.128753  0.059126
c97  -2.128753  0.059126

重构

一旦您了解了这种方法的威力，就可以将上述代码重构为可重用的函数，例如：

def ttest(x, y):
    return stats.ttest_ind(x, y, equal_var=False)

def apply_test(x, subgroup='tow', value='daily_avg', key1='week', key2='weekend', test=ttest):
    g = x.groupby(subgroup).agg({value: list})
    r = test(g.loc[key1, value], g.loc[key2, value])
    return pd.Series({k: getattr(r, k) for k in r._fields})

T = df.groupby('id').apply(apply_test, subgroup='anotherbucket', key1='experience', key2='reference', value='threshold')

它允许您根据需要调整统计测试和数据帧列