@mealbahrani

Here https://plot.ly/python/linear-fits/ is an example of plotting the linear fit.

If you want a plot similar to that generated by `seaborn.regplot`

, simply define:

```
rg=sns.regplot(your data)
```

and extract from `rg`

the regression line data and the Path(s) bounding the confidence interval band, to plot them via Plotly:

```
X=rg.get_lines()[0].get_xdata()# x-coordinate of points along the regression line
Y=rg.get_lines()[0].get_ydata()# y-coordinate
P=rg.get_children()[1].get_paths()#The list of Path(s) bounding the shape of 95% confidence interval-transparent
```

Define two traces of type `scatter`

: one for your data points and the second for the regression line, defined by X and Y.

To get the transparent confidence interval band along the regression line, extract the path describing the boundary of that band as follows:

```
p_codes={1:'M', 2: 'L', 79: 'Z'}#dict to get the Plotly codes for commands to define the svg path
path=''
for s in P[0].iter_segments():
c=p_codes[s[1]]
xx, yy=s[0]
path+=c+str('{:.5f}'.format(xx))+' '+str('{:.5f}'.format(yy))
shapes=[dict(type='path',
path=path,
line=dict(width=0.1,color='rgba(68, 122, 219, 0.25)' ),
fillcolor='rgba(68, 122, 219, 0.25)')]
layout['shapes']=shapes
```

The resulting figure looks like this one:

https://plot.ly/~empet/14628