ANOVA dla grup w ramce danych za pomocą scipy

Question

Mam ramkę danych w następujący sposób. Muszę zrobić ANOVA na tym między trzema warunkami. Dataframe wygląda następująco:

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

muszę zrobić testu ANOVA pomiędzy tak i Non, non i ponownie i tak i RE, warunkach od warunków Names. wiem, że mogę to zrobić tak,

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

Następnie wykonaj jedną ANOVA, jak następuje,

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

Ale byłoby wspaniale, jeśli istnieje eleganckie rozwiązanie jako mojego początkowego ramki danych jest duża i ma wiele imion i warunki do porównania między

Answer 1

Rozważmy następujący przykładowy DataFrame:

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 

następujące grupuje DataFrame przez nazwy, a następnie przechodzi w każdej grupie stanu do 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 

Dla testów post-hoc można wykorzystać statsmodels (jak wyjaśniono here):

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