问题描述

有许多问题和答案以某种方式触及这个主题.通过这个贡献，我想清楚地说明为什么像 marker = {'color' : 'red'} 这样简单的方法适用于 plotly.graph_objects (go)，但 color='red' 不会用于 plotly.express (px) 尽管颜色是 px.Line 和 的属性>px.Scatter.而且我想证明为什么它不那么棒.

所以，如果 px 应该是

颜色由名为

情节2:

代码2:

将 plotly.express 导入为 px将熊猫导入为 pd# 数据框，输入df = px.data.gapminder()df=df.query("year==2007")px.scatter(df, x="gdpPercap", y="lifeExp",颜色='大陆'，大小='流行'，)

为了说明上述方法的灵活性，我们首先更改颜色顺序.由于我们对于初学者只显示 one 类别和 one 颜色，因此您必须等待后续步骤才能看到实际效果.但是现在按照第 1 步使用 color_discrete_sequence=px.colors.qualitative.Alphabet 是相同的图:

1. 用

color_discrete_sequence=px.colors.qualitative.Alphabet

现在，让我们将 Alphabet 颜色序列中的颜色应用到不同的大陆:

2. 使用 color 参数

color = '大陆'

如果您像我一样认为这种特定的颜色序列很容易看，但可能有点难以区分，您可以将您选择的颜色分配给一个或多个大洲，如下所示:

3. 使用

color_discrete_map={亚洲":'红色'}

这非常棒:现在您可以更改序列并为特别有趣的变量选择任何您喜欢的颜色.但是，如果您想将特定颜色分配给更大的子集，上述方法可能会有点乏味.因此，您也可以通过

5. 使用 rgba() 颜色代码设置不透明度.

现在让我们后退一步.如果您认为 red 很适合亚洲，但可能有点过于强烈，您可以使用 rgba 颜色调整不透明度，例如 'rgba(255,0,0,0.4)' 得到这个:

最后一张图的完整代码:

将 plotly.express 导入为 px将熊猫导入为 pd# 数据框，输入df = px.data.gapminder()df=df.query("year==2007")px.scatter(df, x="gdpPercap", y="lifeExp",color_discrete_sequence=px.colors.qualitative.Alphabet,颜色='大陆'，大小='流行'，color_discrete_map={亚洲":'rgba(255,0,0,0.4)'})

如果您认为我们现在变得有点过于复杂，您可以再次覆盖所有设置:

6. 覆盖所有设置:

.update_traces(marker=dict(color='red'))

这让我们回到了开始的地方.我希望你会发现这很有用！

包含所有可用选项的完整代码段:

# 导入将 plotly.express 导入为 px将熊猫导入为 pd# 数据框df = px.data.gapminder()df=df.query("year==2007")子集 = {亚洲"、欧洲"、大洋洲"}group_color = {i: 'red' for i in subset}# plotly 表示散点图px.scatter(df, x="gdpPercap", y="lifeExp",大小='流行'，颜色='大陆',color_discrete_sequence=px.colors.qualitative.Alphabet,#color_discrete_map=group_colorcolor_discrete_map={亚洲":'rgba(255,0,0,0.4)'})#.update_traces(marker=dict(color='red'))

There are many questions and answers that touch upon this topic one way or another. With this contribution I'd like to clearly show why an easy approch such as marker = {'color' : 'red'} will work for plotly.graph_objects (go), but color='red' will not for plotly.express (px) although color is an attribute of both px.Line and px.Scatter. And I'd like to demonstrate why it's awesome that it doesn't.

So, if px is supposed to be the easiest way to make a plotly figure, then why does something as apparently obvious as color='red' return the error

ValueError: Value of 'color' is not the name of a column in 'data_frame'.

To put it short, it's because color in px does not accept an arbitrary color name or code, but rather a variable name in your dataset in order to assign a color cycle to unique values and display them as lines with different colors.

Let me demonstrate by applyig a gapminder dataset and show a scatterplot of Life expectancy versus GDP per capita for all (at least most) countries across the world as of 2007. A basic setup like below will produce the following plot

:

The color is set by a cycle named plotly but is here specified using marker = {'color' : 'red'}

Figure2,code:

import plotly.graph_objects as go

df = px.data.gapminder()
df=df.query("year==2007")

fig = go.Figure()
fig.add_traces(go.Scatter(x=df['gdpPercap'], y=df["lifeExp"],
                          mode = 'markers',
                          marker = {'color' : 'red'}
                         ))
fig.show()

So let's try this with px, and assume that color='red' would do the trick:

:

# imports
import plotly.express as px
import pandas as pd

# dataframe
df = px.data.gapminder()
df=df.query("year==2007")

# plotly express scatter plot
px.scatter(df, x="gdpPercap", y="lifeExp",
           color = 'red',
          )

Result:

ValueError: Value of 'color' is not the name of a column in 'data_frame'. Expected one of ['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap', 'iso_alpha', 'iso_num'] but received: red

So what's going on here?

First, if an explanation of the broader differences between go and px is required, please take a look here and here. And if absolutely no explanations are needed, you'll find a complete code snippet at the very end of the answer which will reveal many of the powers with colors in plotly.express

Part 1: The Essence:

It might not seem so at first, but there are very good reasons why color='red' does not work as you might expect using px. But first of all, if all you'd like to do is manually set a particular color for all markers you can do so using .update_traces(marker=dict(color='red')) thanks to pythons chaining method. But first, lets look at the deafult settings:

1.1Plotlyexpressdefaults

Plot2:

Code2:

import plotly.express as px
import pandas as pd

# dataframe, input
df = px.data.gapminder()
df=df.query("year==2007")

px.scatter(df, x="gdpPercap", y="lifeExp",
           color = 'continent',
           size='pop',
          )

To illustrate the flexibility of the methods above, lets first just change the color sequence. Since we for starters are only showing one category and one color, you'll have to wait for the subsequent steps to see the real effects. But here's the same plot now with color_discrete_sequence=px.colors.qualitative.Alphabet as per step 1:

1. Change the color sequence used by px with

color_discrete_sequence=px.colors.qualitative.Alphabet

Now, let's apply the colors from the Alphabet color sequence to the different continents:

2. Assign different colors to different variables with the color argument

color = 'continent'

If you, like me, think that this particular color sequence is easy on the eye but perhaps a bit indistinguishable, you can assign a color of your choosing to one or more continents like this:

3. customize one or more variable colors with

color_discrete_map={"Asia": 'red'}

And this is pretty awesome: Now you can change the sequence and choose any color you'd like for particularly interesting variables. But the method above can get a bit tedious if you'd like to assign a particular color to a larger subset. So here's how you can do that too with a dict comprehension:

4. Assign colors to a group using a dict comprehension and color_discrete_map

# imports
import plotly.express as px
import pandas as pd

# dataframe
df = px.data.gapminder()
df=df.query("year==2007")

subset = {"Asia", "Europe", "Oceania"}
group_color = {i: 'red' for i in subset}

# plotly express scatter plot
px.scatter(df, x="gdpPercap", y="lifeExp",
           size='pop',
           color='continent',
           color_discrete_sequence=px.colors.qualitative.Alphabet,
           color_discrete_map=group_color
          )

5. Set opacity using rgba() color codes.

Now let's take one step back. If you think red suits Asia just fine, but is perhaps a bit too strong, you can adjust the opacity using a rgba color like 'rgba(255,0,0,0.4)' to get this:

Completecodeforthelastplot:

import plotly.express as px
import pandas as pd

# dataframe, input
df = px.data.gapminder()
df=df.query("year==2007")

px.scatter(df, x="gdpPercap", y="lifeExp",
           color_discrete_sequence=px.colors.qualitative.Alphabet,
           color = 'continent',
           size='pop',
           color_discrete_map={"Asia": 'rgba(255,0,0,0.4)'}
          )

And if you think we're getting a bit too complicated by now, you can override all settings like this again:

6. Override all settings with:

.update_traces(marker=dict(color='red'))

And this brings us right back to where we started. I hope you'll find this useful!

Completecodesnippetwithalloptionsavailable:

# imports
import plotly.express as px
import pandas as pd

# dataframe
df = px.data.gapminder()
df=df.query("year==2007")

subset = {"Asia", "Europe", "Oceania"}
group_color = {i: 'red' for i in subset}

# plotly express scatter plot
px.scatter(df, x="gdpPercap", y="lifeExp",
           size='pop',
           color='continent',
           color_discrete_sequence=px.colors.qualitative.Alphabet,
           #color_discrete_map=group_color
           color_discrete_map={"Asia": 'rgba(255,0,0,0.4)'}
          )#.update_traces(marker=dict(color='red'))

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