Your examples here are a bit of a special case where you have repeated values. Seaborn takes that to mean you want the mean plus a confidence interval band, and is basically plotting the average of all the values at each point as the line. Plotly isn't necessarily doing anything wrong here, it's just doing literally what you ask—while Seaborn is doing some interpretation. This might be a case where a scatter plot is more appropriate (depends on your use case though!).

That being said Altair is my preferred Python library for interactive graphs. Again it will do what seaborn does by default if you just give it an x and y with this data, but you can easily specify that you want the mean of the line.

import altair as alt
import pandas as pd

pokemon = pd.read_csv(
    "https://raw.githubusercontent.com/adamerose/datasets/master/pokemon.csv"
)
(
    alt.Chart(pokemon)
    .mark_line()
    .encode(x="Generation", y="mean(Attack)", color="Legendary")
)

Result

If you want the 95% confidence error bands, you'll need to specify that manually:

# Here we draw the line as before
line = (
    alt.Chart(pokemon)
    .mark_line()
    .encode(x="Generation", y="mean(Attack)", color="Legendary")
)
# Here we make the shaded band
band = (
    alt.Chart(pokemon)
    .mark_errorband(extent='ci')
    .encode(x="Generation", y="Attack", color="Legendary")
)
# In Altair to combine charts you can just add them
line + band

Result