具有古老的PairGrid图的ipywidgets

Question

In a Jupyter Notebook I am visualizing the Iris dataset with seaborn in combination with ipywidgets. That works fine, except that is not that fast because the plots have to be rendered every time you select a new combination of the species 'versicolor', 'virginica' and 'setosa'. See first code block.

So I tried to speed up the interaction by pre-processing the plots for each combination of species and storing them in a dictionary. See second code block. The dictionary seems to contain all plots, but they don't show.

Any suggestions how to fix this?

First code block:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from ipywidgets import *

sns.set(style="white")
iris = sns.load_dataset("iris")

def iris_pg(species):
    g = sns.PairGrid(iris[iris.species.isin(species)], diag_sharey=False)
    g.map_lower(sns.kdeplot)
    g.map_upper(sns.scatterplot)
    g.map_diag(sns.kdeplot, lw=3)
    return plt.show()

interact(iris_pg,
         species = widgets.SelectMultiple(options=iris.species.unique(),
                                          value=tuple(iris.species.unique()[-2:]),
                                          rows=len(iris.species.unique()),
                                          description='species',
                                          disabled=False))

Second code block:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from ipywidgets import *
from itertools import combinations

sns.set(style="white")
iris = sns.load_dataset("iris")

all_combinations = list()
for i in range(1, len(iris.species.unique()) + 1):
    for combi in combinations(iris.species.unique(), i):
        all_combinations.append(combi)

all_plots = dict()
for i in all_combinations:
    all_plots[i] = sns.PairGrid(iris[iris.species.isin(i)], diag_sharey=False)
    all_plots[i].map_lower(sns.kdeplot)
    all_plots[i].map_upper(sns.scatterplot)
    all_plots[i].map_diag(sns.kdeplot, lw=3)

def iris_pg(species):
    all_plots[species]
    return plt.show()

options = iris.species.unique()
value = tuple(iris.species.unique()[-2:])
rows = len(iris.species.unique())

interact(iris_pg,
         species = widgets.SelectMultiple(options=options,
                                          value=value,
                                          rows=rows,
                                          description='species',
                                          disabled=False))

Answer 1

Based on the answer to this question , this is the solution for optimizing the performance of the interaction.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from ipywidgets import *
from itertools import combinations

sns.set(style="white")
iris = sns.load_dataset("iris")

all_combinations = list()
for i in range(1, len(iris.species.unique()) + 1):
    for combi in combinations(iris.species.unique(), i):
        all_combinations.append(combi)

all_plots = dict()
for i in all_combinations:
    all_plots[i] = sns.PairGrid(iris[iris.species.isin(i)], diag_sharey=False)
    all_plots[i].map_lower(sns.kdeplot)
    all_plots[i].map_upper(sns.scatterplot)
    all_plots[i].map_diag(sns.kdeplot, lw=3)
    plt.close() # <-- added

def iris_pairgrid(species):
    return all_plots[species].fig # <-- added .fig

o = iris.species.unique()
v = tuple(iris.species.unique()[-2:])
r = len(iris.species.unique())

interact(iris_pairgrid,
         species = widgets.SelectMultiple(options=o,
                                          value=v,
                                          rows=r,
                                          description='species',
                                          disabled=False))