In [1]:

    

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline


    

In [2]:

    

info = {'VAR': ['Total 1,25(OH)D', 'Site', 'Race', 'Total 25(OH)D', ' Oral Antibiotic Use'],
        'ES_IND': [0.045545564, 0.031161839, 0.027166031, 0.001210949, 0.021151959],
        'ES_CUL': [0.04554556, 0.02964010, 0.02223142, 0.01265013, 0.01270502]}
dat = pd.DataFrame(info, columns=['VAR', 'ES_IND', 'ES_CUL'])


    

In [3]:

    

dat


    

    
Out[3]:







  

    

      

      
VAR
      
ES_IND
      
ES_CUL
    
  
  

    

      
0
      
Total 1,25(OH)D
      
0.045546
      
0.045546
    
    

      
1
      
Site
      
0.031162
      
0.029640
    
    

      
2
      
Race
      
0.027166
      
0.022231
    
    

      
3
      
Total 25(OH)D
      
0.001211
      
0.012650
    
    

      
4
      
Oral Antibiotic Use
      
0.021152
      
0.012705
    
  

In [4]:

    

sns.set(style="whitegrid")

# Plot cumulative effect size
sns.set_color_codes("muted")
ax = sns.barplot(x="ES_CUL", y="VAR", data=dat,
            label="Dependent", color="b")
ax.set_xlabel('Effect Size', fontsize=16)
ax.set_ylabel('Non-redundant Covariates', fontsize=16)

ax = ax.get_figure()
ax.tight_layout()
ax.savefig('../figures/RDA_alpha.pdf')
ax.savefig('../figures/RDA_alpha.png')


    

In [ ]: