Plotly：如何将均值和标准差添加到 create_displot 图？

Question

I am trying to make changes to the histogram and the normal distribution curve here that is made using the create_distplot fuction in plotly. I could not find anything which allows me to add more traces to this plot. Is there any other way to achieve the same results using any other plotly function?

GAIA = pd.read_csv(r'C:\Users\Admin\Desktop\New folder\6 SEM Python\WORK\Astrometry\DistancePM.csv')
    df = pd.DataFrame(GAIA, columns = ['ra','dec','rest','b_rest','B_rest','pmra','pmra_error','pmdec','pmdec_error','PM'])
    ra = df['ra'].tolist()
    dec = df['dec'].tolist()
    rest = df['rest'].tolist()
    b_rest = df['b_rest'].tolist()
    B_rest = df['B_rest'].tolist()
    pmra = df['pmra'].tolist()
    pmra_E = df['pmra_error'].tolist()
    pmdec = df['pmdec'].tolist()
    pmdec_E = df['pmdec_error'].tolist()
    PM = df['PM'].tolist() 
    PM1 = []
    c = 0
    #Here we are onlly taking the range from tehabove contour plot where there is a 
    #clustering of data points x = [-4.5, 1.5] and y = [3, 1]
    for i in range(len(PM)):
        if (PM[i]<100 and pmra[i]>-4.5 and pmra[i]<1.5 and pmdec[i]>1 and pmdec[i]<3):
            PM1.append(PM[i])
            c+=1
    group_labels = ['Proper Motion']
    color = ['#636EFA', '#EF553B', '#00CC96', '#AB63FA', '#FFA15A', '#19D3F3', '#FF6692', '#B6E880', '#FF97FF', '#FECB52']    
    fig = ff.create_distplot(
        [PM1], 
        group_labels,
        bin_size = 0.05,
        curve_type='normal',
        colors = color
    )
    fig.update_layout(
        title = 'Proper Motion Histogram + Gaussian distribution ',
        xaxis = dict(
            title='Proper Motion'
        ),
        yaxis = dict(
            title='Density'
        ),
        template = 'plotly_dark',
        showlegend = True
    )
    fig.show()
    print(c)[![enter image description here][1]][1]
    
    
      [1]: https://i.stack.imgur.com/lOBvY.png

Answer 1

You haven't specified how you'd like to display your added data, so I can only assume that this is what you're looking for:

If that's the case you can calculate your measures using np.mean() and add that to the figure using:

fig.add_shape(type="line",x0=mean, x1=mean, y0 =0, y1=0.4 , xref='x', yref='y',
               line = dict(color = 'blue', dash = 'dash'))

Here's a complete code snippet with mean +/- one standard deviation as well:

import plotly.figure_factory as ff
import numpy as np
np.random.seed(1)

x = np.random.randn(1000)
hist_data = [x]
group_labels = ['distplot'] # name of the dataset

mean = np.mean(x)
stdev_pluss = np.std(x)
stdev_minus = np.std(x)*-1

fig = ff.create_distplot(hist_data, group_labels)
fig.add_shape(type="line",x0=mean, x1=mean, y0 =0, y1=0.4 , xref='x', yref='y',
               line = dict(color = 'blue', dash = 'dash'))
fig.add_shape(type="line",x0=stdev_pluss, x1=stdev_pluss, y0 =0, y1=0.4 , xref='x', yref='y',
               line = dict(color = 'red', dash = 'dash'))
fig.add_shape(type="line",x0=stdev_minus, x1=stdev_minus, y0 =0, y1=0.4 , xref='x', yref='y',
               line = dict(color = 'red', dash = 'dash'))
fig.show()