Why so much difference in SE areas in these graphs I am using ggplot with Python for showing regression/correlation. With method='lm' (means "linear model"), I get following graph: 

And with method='loess', I get following: 

The width of SE area is much wider with loess method than with lm method. Is this expected or is there an error somewhere?
Following is Python code to get above figures:
from ggplot import *
print (ggplot(aes(x='SL', y='PW'), data=irisdf) + \
    geom_point(alpha=0.3) + \
    stat_smooth(colour="black", se=True, method='lm')) 
    # or method='loess' in above line
plt.show()

 A: This is straight up expected behavior for LOESS/LOWESS (and other scatterplot smoothers/nonparametric regression methods).
LOESS (LOcally Estimated Scatterplot Smoother) more or less estimates the value of y using only some fraction of the x observations for a small stretch of x values, it repeats that estimation by shifting that 'small stretch' until all observed values of x have been covered. The result is:


*

*Not assuming a linear relationship between y and x, and (importantly for your question)

*Less confidence about the line of estimates.


A few additional points 


*

*This greater uncertainty about the line of estimates does not mean that nonparametric regression must have lower power than the corresponding linear regression: that is only true if the relationship between y and x is approximately linear (examine the size of the individual residuals from the best fitting straight line through a scattering of y data nonlinearly related to x to get a sense of why).

*LOESS and LOWESS, along with GAMs and other nonparametric regression models all rely on the  'small stretch' of x values mentioned above. This can be expressed as 'bandwidth' or 'span' (which describe the proportion of the observed total range of x values to be included in each estimation), or 'k nearest neighbors' (an absolute number of observed points on the x axis to include).

*When trying to decide whether to use a linear or nonparametric regression model I start with the latter, and ask whether a straight line will fit within the confidence band of the nonparametric regression; if yes, then I proceed to use linear regression, if no, I am done, unless I need parametric estimates for some reason (e.g., statistical inference, communication of model results, model transport to a different data set) in which case I proceed to use nonlinear least squares for a reasonable functional form as informed by the shape of the nonparametric model. NB: I am leaving a lot out about various parametric curve-fitting approaches here.
A: I think the answer is that your two graphs measure two completely different Standard Errors and related Confidence Intervals.  
The first graph represents a Standard Error around the mean observation representing the actual straight regression line.  By definition, this set of Confidence Intervals are going to be very narrow around such regression line.  As you can observe these Confidence Intervals include just a very small fraction of the data points, instead of the customary 95% of such data points when the Confidence Intervals use + or - 1.96 Standard Errors. 
The second graph has what looks like more traditional much wider Standard Errors and Confidence Intervals that capture 95% or more of all data points within your model.  I think this second set of Confidence Intervals are sometimes called Prediction Intervals.  
The two graphs are not wrong.  They are both correct.  They just represent something completely different that people confuse all the time. 
