python - Plotly Express 条形图中具有不同组大小的组条

Question

考虑以下数据框，称为data：

“教师”列中只有两个元素出现两次，其他元素只出现一次。
我用 Plotly Express 制作了一个条形图：

import plotly.express as px
px.bar(data.sort_values("start_time", ascending=False), x="teacher", y="start_time", color="start_time",
       color_continuous_scale="Bluered", barmode="group")

以下是输出：

我希望酒吧彼此相邻，而不是堆叠。我认为这px会使它们堆叠起来（与他们文档中的行为相反），因为我没有每个老师的出现次数相同。

• 那是对的吗？
• 我该如何解决？

标签： pythonplotplotlybar-chartplotly-python

Answer 1

根据这个论坛帖子，正在发生的事情是将plotly.express其解释start_time为连续变量，这就是为什么您会得到一个颜色条，但随后又回到堆叠条形而不是对它们进行分组。

正如@Emmanuelle 所建议的那样，您可以通过创建一个新的 start_time 列来解决这个问题，该列是一个名为 的字符串start_time_str，然后将此列传递给color参数。这迫使 plotly.express 将此变量解释为离散的。但是，您将丢失彩条并获得图例：

data['start_time_str'] = data['start_time'].astype('str')
fig = px.bar(data.sort_values("start_time", ascending=False), x="teacher", y="start_time", color="start_time_str",color_continuous_scale="Bluered", barmode="group")

因此，假设您要保留颜色条并堆叠条形，您将需要更复杂的解决方法。

您可以使用 plotly.express 绘制第一个栏以便获得颜色栏，然后使用fig.add_trace将第二个栏添加为graph_object. 添加第二个条时，您需要指定颜色，为此，您需要一些辅助函数，例如normalize_color_val将这个条的 y 值转换为相对于比例尺上的数据的归一化颜色值0 到 1， get_color当您传递色阶名称和标准化值时，它会返回条形的颜色（作为 rgb 字符串）。

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

data = pd.DataFrame(
    {'teacher':['Lingrand','Milanesio','Menin','Malot','Malot','Schminke','Cornelli','Milanesio','Marchello','Menin','Huet'],
    'start_time':[12,12,5,0,5,0,4,8,-1,0,4]}
)

# This function allows you to retrieve colors from a continuous color scale
# by providing the name of the color scale, and the normalized location between 0 and 1
# Reference: https://stackoverflow.com/questions/62710057/access-color-from-plotly-color-scale

def get_color(colorscale_name, loc):
    from _plotly_utils.basevalidators import ColorscaleValidator
    # first parameter: Name of the property being validated
    # second parameter: a string, doesn't really matter in our use case
    cv = ColorscaleValidator("colorscale", "")
    # colorscale will be a list of lists: [[loc1, "rgb1"], [loc2, "rgb2"], ...] 
    colorscale = cv.validate_coerce(colorscale_name)
    
    if hasattr(loc, "__iter__"):
        return [get_continuous_color(colorscale, x) for x in loc]
    return get_continuous_color(colorscale, loc)
        

# Identical to Adam's answer
import plotly.colors
from PIL import ImageColor

def get_continuous_color(colorscale, intermed):
    """
    Plotly continuous colorscales assign colors to the range [0, 1]. This function computes the intermediate
    color for any value in that range.

    Plotly doesn't make the colorscales directly accessible in a common format.
    Some are ready to use:
    
        colorscale = plotly.colors.PLOTLY_SCALES["Greens"]

    Others are just swatches that need to be constructed into a colorscale:

        viridis_colors, scale = plotly.colors.convert_colors_to_same_type(plotly.colors.sequential.Viridis)
        colorscale = plotly.colors.make_colorscale(viridis_colors, scale=scale)

    :param colorscale: A plotly continuous colorscale defined with RGB string colors.
    :param intermed: value in the range [0, 1]
    :return: color in rgb string format
    :rtype: str
    """
    if len(colorscale) < 1:
        raise ValueError("colorscale must have at least one color")

    hex_to_rgb = lambda c: "rgb" + str(ImageColor.getcolor(c, "RGB"))

    if intermed <= 0 or len(colorscale) == 1:
        c = colorscale[0][1]
        return c if c[0] != "#" else hex_to_rgb(c)
    if intermed >= 1:
        c = colorscale[-1][1]
        return c if c[0] != "#" else hex_to_rgb(c)

    for cutoff, color in colorscale:
        if intermed > cutoff:
            low_cutoff, low_color = cutoff, color
        else:
            high_cutoff, high_color = cutoff, color
            break

    if (low_color[0] == "#") or (high_color[0] == "#"):
        # some color scale names (such as cividis) returns:
        # [[loc1, "hex1"], [loc2, "hex2"], ...]
        low_color = hex_to_rgb(low_color)
        high_color = hex_to_rgb(high_color)

    return plotly.colors.find_intermediate_color(
        lowcolor=low_color,
        highcolor=high_color,
        intermed=((intermed - low_cutoff) / (high_cutoff - low_cutoff)),
        colortype="rgb",
    )

def normalize_color_val(color_val, data=data):
    return (color_val - min(data.start_time)) / (max(data.start_time - min(data.start_time)))

## add the first bars
fig = px.bar(
    data.sort_values("start_time", ascending=False).loc[~data['teacher'].duplicated()],
    x="teacher", y="start_time", color="start_time",
    color_continuous_scale="Bluered", barmode="group"
)

## add the other bars, these will automatically be grouped
for x,y in data.sort_values("start_time", ascending=False).loc[data['teacher'].duplicated()].itertuples(index=False):
    fig.add_trace(go.Bar(
        x=[x],
        y=[y],
        marker=dict(color=get_color('Bluered', normalize_color_val(y))),
        hovertemplate="teacher=%{x}<br>start_time=%{y}<extra></extra>",
        showlegend=False
    ))

fig.show()