Set confidence intervals for error bars plot in matplotlib-CodePudding

I have this dataset:

mydf = pd.DataFrame({'Feature':['Pysch','Physio'],'log_or':[0.3126,0.2022],
'se':[0.0712,0.0568], 'conf_low':[0.1729,0.0907], 'conf_high':[0.4522, 0.3136]})
mydf = mydf.sort_values(by='log_or')
mydf
    Feature log_or  se      conf_low  conf_high
1   Physio  0.2022  0.0568  0.0907    0.3136
0   Pysch   0.3126  0.0712  0.1729    0.4522

And I want to create an error bar plot using my calculated confidence intervals in con_low and conf_high

I tried this at the beginning but I can see that the intervals don't cover my calculated confidence intervals:

plt.errorbar(mydf['log_or'], mydf['Feature'], 
xerr=mydf['se'], marker='s', mfc='Tomato')
plt.show()

You can see that, for example, in the Physio variable the error bar goes from 0.14 to 0.26 in the image approximately, but my tabulated confidence intervals go from 0.091 to 0.316.

So I tried to set up my custom intervals, with this:

lowr = mydf['conf_low'].to_numpy()
uppr = mydf['conf_high'].to_numpy()
intervals = [lowr, uppr]
plt.errorbar(mydf['log_or'], mydf['Feature'], xerr=intervals, marker='s', mfc='Tomato')
plt.show()

Now my variable Physio interval goes from 0.1 to 0.5 approx, which is wrong. Now, what I am doing wrong? How can I use my custom intervals to this plot?

CodePudding user response：

I think you are misunderstanding what the values passed to xerr are meant to represent. Have a look at the