example code for method 1 - using subplots, update_yaxes modifies all three axes

import plotly.graph_objects as go
from plotly import data
from plotly.subplots import make_subplots

df = data.stocks()

fig = make_subplots(rows=3, cols=1, shared_xaxes=True, vertical_spacing=0.03)
layout = dict(
hoversubplots="axis",
title="Stock Price Changes",
hovermode="x",
grid=dict(rows=3, columns=1),
)

fig.add_trace(go.Scatter(x=df["date"], y=df["AAPL"], name="Apple"), row=1, col=1)
fig.add_trace(go.Scatter(x=df["date"], y=df["GOOG"], name="Google"), row=2, col=1)
fig.add_trace(go.Scatter(x=df["date"], y=df["AMZN"], name="Amazon"), row=3, col=1)
fig.update_layout(layout)
fig.update_yaxes(dict(rangemode='tozero'))

fig.show()

example code for method 2 - specifying axis directly, update_yaxes only modifies the behavior of the first subplot

import plotly.graph_objects as go
import pandas as pd
from plotly import data

df = data.stocks()

layout = dict(
hoversubplots="axis",
title=dict(text="Stock Price Changes"),
hovermode="x",
grid=dict(rows=3, columns=1),
)

data = [
go.Scatter(x=df["date"], y=df["AAPL"], xaxis="x", yaxis="y", name="Apple"),
go.Scatter(x=df["date"], y=df["GOOG"], xaxis="x", yaxis="y2", name="Google"),
go.Scatter(x=df["date"], y=df["AMZN"], xaxis="x", yaxis="y3", name="Amazon"),
]

fig = go.Figure(data=data, layout=layout)
fig.update_yaxes(dict(rangemode='tozero'))
fig.show()

Update_yaxes’ documentation states that the supposed behavior is to modify all y-axes at once, link

“By default, these methods apply to all of the x axes or y axes in the figure. The row and col arguments can be used to control which axes are targeted by the update.”

method 1’s fig.layout:

method 2’s fig.layout:

The function is able to recognise that it needs to modify yaxis, yaxis2, etc, but only when creating subplots via method 1.