如何在绘图网格中的 row/column 上设置属性?

How to set properties on a row/column in a grid of plotly plots?

假设我在每行上绘制 2 个图表,共 10 行,使用 plotly:

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

N=10
fig = make_subplots(rows=N, cols=2)

fig.add_trace(
    go.Scatter(x=x, y=y),
    row=1, col=1
)

fig.add_trace(
    go.Candlestick(
        x=df_kline.index,
        open=df_kline['O'],
        high=df_kline['H'],
        low=df_kline['L'],
        close=df_kline['C']
    ),
    row=1, col=2
)

:
fig.show()

如何为每一行设置一个 yaxis_title

如何将整个第一列的 y 轴范围设置为 [1,10],并且只显示绘图底部的刻度标签?

我希望这是一个问题而不是两个问题,因为它处理的是 group-by-row / group-by-col。


脚注:

根据接受的答案中的评论,可以在多个子图中设置设置:

subplot_settings = {
    'rangeslider_visible': True,
    'rangeslider_thickness': 0.05
}
kwargs = {
    f'xaxis{k}' : subplot_settings
        for k in range(2, 2*N, 2)
}
fig.update_layout(**kwargs)

(未测试)

由于没有提供数据,我用一个特定的股票价格用四个子图来应对挑战;第一行中每一行的 y-axis 的标题和范围可以在 y-axis 设置中设置。此外,在子图的设置部分,如果将​​共享轴设置为 x-axis,则只有底部 x-axis 可用。

from plotly.subplots import make_subplots
import plotly.graph_objects as go
import numpy as np
import pandas as pd

x = np.linspace(0,1, 100)
y = np.cumsum(x)

import yfinance as yf
df_kline = yf.download("AAPL", start="2021-01-01", end="2021-03-01")
df_kline.rename(columns={'Open':'O','High':'H','Low':'L','Close':'C'}, inplace=True)

N=2
fig = make_subplots(rows=N, cols=2, 
                    shared_xaxes=True,    )# vertical_spacing=0.1

fig.add_trace(
    go.Scatter(x=x, y=y),
    row=1, col=1
)

fig.add_trace(
    go.Candlestick(
        x=df_kline.index,
        open=df_kline['O'],
        high=df_kline['H'],
        low=df_kline['L'],
        close=df_kline['C'],

    ),
    row=1, col=2,
)

fig.add_trace(
    go.Scatter(x=x, y=y),
    row=2, col=1
)

fig.add_trace(
    go.Candlestick(
        x=df_kline.index,
        open=df_kline['O'],
        high=df_kline['H'],
        low=df_kline['L'],
        close=df_kline['C'],
    ),
    row=2, col=2
)

fig.update_layout(autosize=False, height=600, width=1000, showlegend=False)

# rangeslider visible false
fig.update_layout(title='Custome subplots',
                  xaxis2=dict(rangeslider=dict(visible=False)),
                  xaxis4=dict(rangeslider=dict(visible=False)))
# yxais customize
fig.update_layout(yaxis1=dict(range=[0,10], title='test'),
                 yaxis3=dict(range=[0,10], title='test2'))
fig.show()