Python 绘制多列子图

Question

我有一个代码如下，它基本上使用plotly并绘制数据框的多列。 下面的代码工作正常。但我想用这个图作为子图。想象一下，有 3 行和 2 列，并希望将此函数的类似图形显示为子图。

def custom_graph_objects(df, item, show=False):
    # Create the Plotly figure
    fig = go.Figure()


    # Add Percentage Change trace with markers and labels
    fig.add_trace(go.Scatter(x=df['vpl_start_week'][df['wh_item_code']==item], y=df['list_price'][df['wh_item_code']==item], mode='lines', name='list_price'))

    fig.add_trace(go.Scatter(x=df['vpl_start_week'][df['wh_item_code']==item], y=df['current_sell_zone1'][df['wh_item_code']==item], mode='lines', name='current_sell_zone1'))

    # Add Percentage Change From Initial trace with markers and labels
    fig.add_trace(go.Scatter(x=df['vpl_start_week'][df['wh_item_code']==item], y=df['percentile_difference'][df['wh_item_code']==item], mode='lines',
                            name='percentile_difference'))

    # Set the title and axis labels
    fig.update_layout(title='Item Price Analysis',
                    xaxis_title='vpl_start_date',
                    yaxis_title='price')
    fig.update_traces(mode="lines", hovertemplate=None)
    fig.update_layout(hovermode="x unified")
    if show:
        fig.show()
    return fig

下面是我尝试的子图代码，但它没有反映我需要的。

r = 2
c = 2
sample_items = random.sample(grand_aggregate['wh_item_code'].unique().tolist(), r*c)
sub_fig = make_subplots(rows=r, cols=c)
pos = [(x+1, y+1) for x in range(r) for y in range(c)]
for i, item in enumerate(sample_items):
    sub_fig.add_trace(go.Scatter(x=grand_aggregate['vpl_start_week'][grand_aggregate['wh_item_code']==item], 
                                 y=grand_aggregate['list_price'][grand_aggregate['wh_item_code']==item], mode='lines', name='list_price'), 
                  row=pos[i][0], col=pos[i][1])
    sub_fig.update_traces(go.Scatter(x=grand_aggregate['vpl_start_week'][grand_aggregate['wh_item_code']==item], 
                                    y=grand_aggregate['current_sell_zone1'][grand_aggregate['wh_item_code']==item], mode='lines', name='current_sell_zone1'), 
                  row=pos[i][0], col=pos[i][1])
    sub_fig.update_traces(go.Scatter(x=grand_aggregate['vpl_start_week'][grand_aggregate['wh_item_code']==item], 
                                    y=grand_aggregate['percentile_difference'][grand_aggregate['wh_item_code']==item], mode='lines', name='percentile_difference'), 
                  row=pos[i][0], col=pos[i][1])
    
sub_fig.update_layout(height=900, width=1200, title_text="Subplots")
sub_fig.show()

非常感谢对此的任何帮助。预先感谢。

Answer 1

plotly 中的子图使用 make_subplots(rows=r, cols=c) 创建一个对象，并将图形设置为子图，该子图是对象的矩阵之一。因此函数中不需要 'fig=go.Figure()'，并且需要Fig作为参数。示例数据有多列，但我创建了一个函数将其扩展为子图，然后创建代码以在循环过程中完成整个过程。

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

df = px.data.stocks()

df.head()
date    GOOG    AAPL    AMZN    FB  NFLX    MSFT
0   2018-01-01  1.000000    1.000000    1.000000    1.000000    1.000000    1.000000
1   2018-01-08  1.018172    1.011943    1.061881    0.959968    1.053526    1.015988
2   2018-01-15  1.032008    1.019771    1.053240    0.970243    1.049860    1.020524
3   2018-01-22  1.066783    0.980057    1.140676    1.016858    1.307681    1.066561
4   2018-01-29  1.008773    0.917143    1.163374    1.018357    1.273537    1.040708

r,c = 3,2
sample_tickers = df.columns.tolist()[1:]
sub_fig = make_subplots(rows=r, cols=c)
pos = [(x+1, y+1) for x in range(r) for y in range(c)]

def custom_graph(df, tick, sub_fig, pos, show=False):
    sub_fig.add_trace(go.Scatter(
        x=df['date'],
        y=df[tick],
        mode='lines'
    ), row=pos[0], col=pos[1])
    return sub_fig

for i,(tick,p) in enumerate(zip(sample_tickers,pos)):
    sub_fig = custom_graph(df, tick, sub_fig, p, show=False)

sub_fig.update_layout(height=900, width=1200, title_text='Subplots')
sub_fig.show()