Python 使用scipy对数据帧内的组进行方差分析

我有一个如下的数据帧。我需要在这三个条件之间做方差分析。数据帧看起来像：

data0 = pd.DataFrame({'Names': ['CTA15', 'CTA15', 'AC007', 'AC007', 'AC007','AC007'], 
    'value': [22, 22, 2, 2, 2,5], 
    'condition':['NON', 'NON', 'YES', 'YES', 'RE','RE']})

我需要在是与否、非与RE、是与RE之间进行方差分析测试，条件来自名称的条件。 我知道我可以这样做

NON=df.query('condition =="NON"and Names=="CTA15"')
no=df.value
YES=df.query('condition =="YES"and Names=="CTA15"')    
Y=YES.value

然后进行单因素方差分析，如下所示：

    from scipy import stats                
    f_val, p_val = stats.f_oneway(no, Y)            
    print ("One-way ANOVA P =", p_val )

---

但如果有任何优雅的解决方案，那就太好了，因为我的初始数据帧很大，并且有许多名称和条件可供比较

请考虑以下示例数据帧：

df = pd.DataFrame({'Names': np.random.randint(1, 10, 1000), 
                   'value': np.random.randn(1000), 
                   'condition': np.random.choice(['NON', 'YES', 'RE'], 1000)})

df.head()
Out: 
   Names condition     value
0      4        RE  0.844120
1      4       NON -0.440285
2      5       YES  0.559497
3      4        RE  0.472425
4      9       YES  0.205906

以下按名称对数据帧进行分组，然后将每个条件组传递给ANOVA：

import scipy.stats as ss
for name_group in df.groupby('Names'):
    samples = [condition[1] for condition in name_group[1].groupby('condition')['value']]
    f_val, p_val = ss.f_oneway(*samples)
    print('Name: {}, F value: {:.3f}, p value: {:.3f}'.format(name_group[0], f_val, p_val))

Name: 1, F value: 0.138, p value: 0.871
Name: 2, F value: 1.458, p value: 0.237
Name: 3, F value: 0.742, p value: 0.479
Name: 4, F value: 2.718, p value: 0.071
Name: 5, F value: 0.255, p value: 0.776
Name: 6, F value: 1.731, p value: 0.182
Name: 7, F value: 0.269, p value: 0.764
Name: 8, F value: 0.474, p value: 0.624
Name: 9, F value: 1.226, p value: 0.297

对于事后测试，您可以使用statsmodels（如上所述）：

名称1平均值的多重比较-Tukey HSD，FWER=0.05 ============================================ 第1组第2组平均上下不合格品 -------------------------------------------- 非RE 0.0086-0.5129 0.5301假 非是0.0084-0.4817 0.4986假 RE是-0.0002-0.5217 0.5214假 -------------------------------------------- 名称2平均值的多重比较-Tukey HSD，FWER=0.05 ============================================ 第1组第2组平均上下不合格品 -------------------------------------------- 非RE-0.0089-0.5299 0.5121假 非是0.083-0.4182 0.5842假 RE是0.0919-0.4008 0.5846假 -------------------------------------------- 名称3平均值的多重比较-Tukey HSD，FWER=0.05 ============================================ 第1组第2组平均上下不合格品 -------------------------------------------- 非RE 0.2401-0.3136 0.7938假 非是0.2765-0.2903 0.8432假 RE是0.0364-0.5052 0.578假 -------------------------------------------- 名称4平均值的多重比较-Tukey HSD，FWER=0.05 ============================================ 第1组第2组平均上下不合格品 -------------------------------------------- 非RE 0.0894-0.5825 0.7613假 非是-0.0437-0.7418 0.6544假 RE是-0.1331-0.6949 0.4287假 -------------------------------------------- 名称5平均值的多重比较-Tukey HSD，FWER=0.05 ============================================ 第1组第2组平均上下不合格品 -------------------------------------------- 非RE-0.4264-0.9495 0.0967假 非是0.0439-0.4264 0.5142假 RE是0.4703-0.0155 0.9561假 -------------------------------------------- 名称6平均值的多重比较-Tukey HSD，FWER=0.05 ============================================ 第1组第2组平均上下不合格品 -------------------------------------------- 非RE 0.0649-0.4971 0.627假 非是-0.406-0.9405 0.1285假 RE是-0.4709-1.0136 0.0717假 -------------------------------------------- 名称7平均值的多重比较-Tukey HSD，FWER=0.05 ============================================ 第1组第2组平均上下不合格品 -------------------------------------------- 非RE 0.3111-0.2766 0.8988假 非是-0.1664-0.7314 0.3987假 RE是-0.4774-1.0688 0.114假 -------------------------------------------- 名称8平均值的多重比较-Tukey HSD，FWER=0.05 ============================================ 第1组第2组平均上下不合格品 -------------------------------------------- 非RE-0.0224-0.668 0.6233假 非是0.0119-0.668 0.6918假 RE是0.0343-0.6057 0.6742假 -------------------------------------------- 名称9平均值的多重比较-Tukey HSD，FWER=0.05 ============================================ 第1组第2组平均上下不合格品 -------------------------------------------- 非RE-0.2414-0.7792 0.2963假 非是0.0696-0.5746 0.7138假 RE是0.311-0.3129 0.935假

from statsmodels.stats.multicomp import pairwise_tukeyhsd
for name, grouped_df in df.groupby('Names'):
    print('Name {}'.format(name), pairwise_tukeyhsd(grouped_df['value'], grouped_df['condition']))

Name 1 Multiple Comparison of Means - Tukey HSD,FWER=0.05 ============================================ group1 group2 meandiff lower upper reject -------------------------------------------- NON RE 0.0086 -0.5129 0.5301 False NON YES 0.0084 -0.4817 0.4986 False RE YES -0.0002 -0.5217 0.5214 False -------------------------------------------- Name 2 Multiple Comparison of Means - Tukey HSD,FWER=0.05 ============================================ group1 group2 meandiff lower upper reject -------------------------------------------- NON RE -0.0089 -0.5299 0.5121 False NON YES 0.083 -0.4182 0.5842 False RE YES 0.0919 -0.4008 0.5846 False -------------------------------------------- Name 3 Multiple Comparison of Means - Tukey HSD,FWER=0.05 ============================================ group1 group2 meandiff lower upper reject -------------------------------------------- NON RE 0.2401 -0.3136 0.7938 False NON YES 0.2765 -0.2903 0.8432 False RE YES 0.0364 -0.5052 0.578 False -------------------------------------------- Name 4 Multiple Comparison of Means - Tukey HSD,FWER=0.05 ============================================ group1 group2 meandiff lower upper reject -------------------------------------------- NON RE 0.0894 -0.5825 0.7613 False NON YES -0.0437 -0.7418 0.6544 False RE YES -0.1331 -0.6949 0.4287 False -------------------------------------------- Name 5 Multiple Comparison of Means - Tukey HSD,FWER=0.05 ============================================ group1 group2 meandiff lower upper reject -------------------------------------------- NON RE -0.4264 -0.9495 0.0967 False NON YES 0.0439 -0.4264 0.5142 False RE YES 0.4703 -0.0155 0.9561 False -------------------------------------------- Name 6 Multiple Comparison of Means - Tukey HSD,FWER=0.05 ============================================ group1 group2 meandiff lower upper reject -------------------------------------------- NON RE 0.0649 -0.4971 0.627 False NON YES -0.406 -0.9405 0.1285 False RE YES -0.4709 -1.0136 0.0717 False -------------------------------------------- Name 7 Multiple Comparison of Means - Tukey HSD,FWER=0.05 ============================================ group1 group2 meandiff lower upper reject -------------------------------------------- NON RE 0.3111 -0.2766 0.8988 False NON YES -0.1664 -0.7314 0.3987 False RE YES -0.4774 -1.0688 0.114 False -------------------------------------------- Name 8 Multiple Comparison of Means - Tukey HSD,FWER=0.05 ============================================ group1 group2 meandiff lower upper reject -------------------------------------------- NON RE -0.0224 -0.668 0.6233 False NON YES 0.0119 -0.668 0.6918 False RE YES 0.0343 -0.6057 0.6742 False -------------------------------------------- Name 9 Multiple Comparison of Means - Tukey HSD,FWER=0.05 ============================================ group1 group2 meandiff lower upper reject -------------------------------------------- NON RE -0.2414 -0.7792 0.2963 False NON YES 0.0696 -0.5746 0.7138 False RE YES 0.311 -0.3129 0.935 False