CODEWITHSUNDEEP
pythonseabornvisualization
schoon
schoon

Reputation: 3334

How do I get the diagonal of sns.pairplot?

OK I am probably being thick, but how do I get just the graphs in the diagonal (top left to bottom right) in a nice row or 2x2 grid of:

import seaborn as sns; sns.set(style="ticks", color_codes=True)
iris = sns.load_dataset("iris")
g = sns.pairplot(iris, hue="species", palette="husl")

TO CLARIFY: I just want these graphs I do not care whether pairplot or something else is used.

Upvotes: 3

Views: 8909

Answers (6)

Yuan Tao
Yuan Tao

Reputation: 467

I just came across the question and get a simple python solution:

for col in iris.columns[:-1]:
    sns.pairplot(iris[[col,"species"]],hue="species", palette="husl")

enter image description here enter image description here enter image description here enter image description here

And then you can get the separate plots. If you want them in one plot just add subplots related codes.

Upvotes: 0

Leonardo Queiroz
Leonardo Queiroz

Reputation: 7

import seaborn as sns
import matplotlib.pyplot as plt
iris = sns.load_dataset("iris")

sns.pairplot(iris, hue="species", corner=True)

enter image description here

Upvotes: -2

ImportanceOfBeingErnest
ImportanceOfBeingErnest

Reputation: 339560

Doing this the seaborn-way would make use of a FacetGrid. For this we would need to convert the wide-form input to a long-form dataframe, such that every observation is a single row. This is done via pandas.melt.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

iris = sns.load_dataset("iris")
df = pd.melt(iris, iris.columns[-1], iris.columns[:-1])

g = sns.FacetGrid(df, col="variable", hue="species", col_wrap=2)
g.map(sns.kdeplot, "value", shade=True)

plt.show()

enter image description here

Upvotes: 6

KRKirov
KRKirov

Reputation: 4004

plt.subplots(2, 2)
for i, col in enumerate(iris.columns[:4]):
    plt.subplot(2, 2, i+1)
    sns.kdeplot(iris.loc[iris['species'] == 'setosa', col], shade=True, label='setosa')
    sns.kdeplot(iris.loc[iris['species'] == 'versicolor', col], shade=True, label='versicolor')
    sns.kdeplot(iris.loc[iris['species'] == 'virginica', col], shade=True, label='virginica')
    plt.xlabel('cm')
    plt.title(col)
    if i == 1:
        plt.legend(loc='upper right')
    else:
        plt.legend().remove()

plt.subplot_tool() # Opens a widget which allows adjusting plot aesthetics

enter image description here

Upvotes: 0

Soumendra Bagh
Soumendra Bagh

Reputation: 56

Why do you even want to do that. The diagonal of the pairplot gives you the distplot of that feature. It will be more effective if you can plot the idividual distplots as subplot or mux them Ex:

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
import seaborn as sns

iris = load_iris()
iris = pd.DataFrame(data=np.c_[iris['data'], iris['target']],
                    columns=iris['feature_names'] + ['target'])

# Sort the dataframe by target
target_0 = iris.loc[iris['target'] == 0]
target_1 = iris.loc[iris['target'] == 1]
target_2 = iris.loc[iris['target'] == 2]

sns.distplot(target_0[['sepal length (cm)']], hist=False, rug=True)
sns.distplot(target_1[['sepal length (cm)']], hist=False, rug=True)
sns.distplot(target_2[['sepal length (cm)']], hist=False, rug=True)

sns.plt.show()

The output will be somewhat like this: Distplot[1]

Read more here : python: distplot with multiple distributions

Upvotes: 1

Harish Vutukuri
Harish Vutukuri

Reputation: 1150

import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="ticks", color_codes=True)
iris = sns.load_dataset("iris")

def hide_current_axis(*args, **kwds):
    plt.gca().set_visible(False)

g = sns.pairplot(iris, hue="species", palette="husl")
g.map_upper(hide_current_axis)
g.map_lower(hide_current_axis)

Output:

Pairplot Diagonal

Upvotes: 0

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