情节:如何制作多条线和阴影区域的图形以显示标准偏差?

发布于 2021-01-29 14:09:59

如何使用Plotly生成带有阴影标准偏差的折线图?我正在尝试实现类似于seaborn.tsplot的功能。任何帮助表示赞赏。
在此处输入图片说明

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  • 面试哥
    面试哥 2021-01-29
    为面试而生,有面试问题,就找面试哥。

    对于pandas数据框中的列数,以下方法完全灵活,并使用plotly默认颜色周期。如果行数超过颜色数,将从一开始就重新使用颜色。截至目前px.colors.qualitative.Plotly,您可以使用px.colors.qualitative以下任何十六进制颜色序列替换:

    Alphabet = ['#AA0DFE', '#3283FE', '#85660D', '#782AB6', '#565656', '#1...
    Alphabet_r = ['#FA0087', '#FBE426', '#B00068', '#FC1CBF', '#C075A6', '...
    [...]
    

    在此处输入图片说明

    完整的代码:

    # imports
    import plotly.graph_objs as go
    import plotly.express as px
    import pandas as pd
    import numpy as np
    
    # sample data in a pandas dataframe
    np.random.seed(1)
    df=pd.DataFrame(dict(A=np.random.uniform(low=-1, high=2, size=25).tolist(),
                        B=np.random.uniform(low=-4, high=3, size=25).tolist(),
                        C=np.random.uniform(low=-1, high=3, size=25).tolist(),
                        ))
    df = df.cumsum()
    
    # define colors as a list 
    colors = px.colors.qualitative.Plotly
    
    # convert plotly hex colors to rgba to enable transparency adjustments
    def hex_rgba(hex, transparency):
        col_hex = hex.lstrip('#')
        col_rgb = list(int(col_hex[i:i+2], 16) for i in (0, 2, 4))
        col_rgb.extend([transparency])
        areacol = tuple(col_rgb)
        return areacol
    
    rgba = [hex_rgba(c, transparency=0.2) for c in colors]
    colCycle = ['rgba'+str(elem) for elem in rgba]
    
    # Make sure the colors run in cycles if there are more lines than colors
    def next_col(cols):
        while True:
            for col in cols:
                yield col
    line_color=next_col(cols=colCycle)
    
    # plotly  figure
    fig = go.Figure()
    
    # add line and shaded area for each series and standards deviation
    for i, col in enumerate(df):
        new_col = next(line_color)
        x = list(df.index.values+1)
        y1 = df[col]
        y1_upper = [(y + np.std(df[col])) for y in df[col]]
        y1_lower = [(y - np.std(df[col])) for y in df[col]]
        y1_lower = y1_lower[::-1]
    
        # standard deviation area
        fig.add_traces(go.Scatter(x=x+x[::-1],
                                    y=y1_upper+y1_lower,
                                    fill='tozerox',
                                    fillcolor=new_col,
                                    line=dict(color='rgba(255,255,255,0)'),
                                    showlegend=False,
                                    name=col))
    
        # line trace
        fig.add_traces(go.Scatter(x=x,
                                  y=y1,
                                  line=dict(color=new_col, width=2.5),
                                  mode='lines',
                                  name=col)
                                    )
    # set x-axis
    fig.update_layout(xaxis=dict(range=[1,len(df)]))
    
    fig.show()
    


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