How to create a consistent plotly color map with dynamic input?-CodePudding

I have some dataframes I want to plot with a variable number of Fruits. When plotting, each dataframe will always have the same Fruit types, even though some may have a value of 0 in their respective row.

df1
Day  Value Fruit
1    5     Banana
1    3     Pear
2    4     Banana
2    2     Pear
3    3     Banana
3    3     Pear

df2  # note all pears 0'd
Day  Value Fruit
1    5     Banana
1    0     Pear
2    4     Banana
2    0     Pear
3    3     Banana
3    0     Pear

# AKA
df1.Fruit.unique() == df2.Fruit.unique()

Plotting code:

import dash_bootstrap_components as dbc
from plotly import express as px

bar_charts = []
for df in dfs:
  # to get a stacked bar chart
  fruit_data = df.groupby(["Day", "Fruit"]).Value.sum().to_frame().reset_index()
  bar_charts.append(
    dcc.Graph(
      figure=px.bar(
        fruit_data,
        x="Day",
        y="Value",
        color="Fruit",
      ),
    )
  )
return dbc.Col(bar_charts)

Plotly seems to be assigning differnt colors to the Fruit between dataframes based on the value of the Fruit. I want the Fruits to always be the same color across graphs regardless of value (e.g. Banana always yellow, Pear always green). HOWEVER, I don't know what Fruits are available until runtime, so I can't just hard code a color map ahead of time.

How do I tell plotly to always color the Fruit the same?

Im sure this is some silly little option im missing.

CodePudding user response：

To make sure that the same colors are assigned to the same Fruit, you can make a color map using fruit_data['Fruit'].unique() and any list of colors like ['yellow', 'green'] or a longer list px.colors.qualitative.Alphabet for color_discrete_map in px.bar like this:

colordict = {f:px.colors.qualitative.Alphabet[i] for i, f in enumerate(fruit_data.Fruit.unique())}
colordict = {f:['yellow', 'green'][i] for i, f in enumerate(fruit_data.Fruit.unique())}

Plot:

And if you're working with a large number of variables and worry about running out of colors, just incorporate the approach described in Plotly: How to increase the number of colors to assure unique colors for all lines like this:

n_colors = len(fruit_data.Fruit.unique())
colorscale = colors = px.colors.sample_colorscale("viridis", [n/(n_colors -1) for n in range(n_colors)])
colordict = {f:colorscale[i] for i, f in enumerate(fruit_data.Fruit.unique())}

Complete code:

import io
import plotly.express as px
import pandas as pd

# data
fruit_data = pd.read_csv(io.StringIO("""Day  Value Fruit
                                1    5     Banana
                                1    3     Pear
                                2    4     Banana
                                2    2     Pear
                                3    3     Banana
                                3    3     Pear"""),sep= '\\s ')

# color assignment:
# colordict = {f:px.colors.qualitative.Alphabet[i] for i, f in enumerate(fruit_data.Fruit.unique())}
colordict = {f:['yellow', 'green'][i] for i, f in enumerate(fruit_data.Fruit.unique())}

# color assignment for a large number of variables:
n_colors = len(fruit_data.Fruit.unique())
colorscale = colors = px.colors.sample_colorscale("viridis", [n/(n_colors -1) for n in range(n_colors)])
colordict = {f:colorscale[i] for i, f in enumerate(fruit_data.Fruit.unique())}

# plotly figure
fig = px.bar(
        fruit_data,
        x="Day",
        y="Value",
        color = 'Fruit',
        color_discrete_map = colordict
      )
fig.show()