python：具有多个分布的distplot

Question

I am using seaborn to plot a distribution plot. I would like to plot multiple distributions on the same plot in different colors:

Here's how I start the distribution plot:

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
iris = load_iris()
iris = pd.DataFrame(data= np.c_[iris['data'], iris['target']],columns= iris['feature_names'] + ['target'])

sns.distplot(iris[['sepal length (cm)']], hist=False, rug=True);

The 'target' column contains 3 values: 0,1,2.

I would like to see one distribution plot for sepal length where target ==0, target ==1, and target ==2 for a total of 3 plots.

Does anyone know how I do that?

Thank you.

Answer 1

The important thing is to sort the dataframe by values where target is 0 , 1 , or 2 .

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)

plt.show()

The output looks like:

If you don't know how many values target may have, find the unique values in the target column, then slice the dataframe and add to the plot appropriately.

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'])

unique_vals = iris['target'].unique()  # [0, 1, 2]

# Sort the dataframe by target
# Use a list comprehension to create list of sliced dataframes
targets = [iris.loc[iris['target'] == val] for val in unique_vals]

# Iterate through list and plot the sliced dataframe
for target in targets:
    sns.distplot(target[['sepal length (cm)']], hist=False, rug=True)

sns.plt.show()

Answer 2

A more common approach for this type of problems is to recast your data into long format using melt, and then let map do the rest.

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'])

# recast into long format 
df = iris.melt(['target'], var_name='cols',  value_name='vals')

df.head()

   target               cols  vals
0     0.0  sepal length (cm)   5.1
1     0.0  sepal length (cm)   4.9
2     0.0  sepal length (cm)   4.7
3     0.0  sepal length (cm)   4.6
4     0.0  sepal length (cm)   5.0

You can now plot simply by creating a FacetGrid and using map:

g = sns.FacetGrid(df, col='cols', hue="target", palette="Set1")
g = (g.map(sns.distplot, "vals", hist=False, rug=True))

Answer 3

I have found a simpler solution using FacetGrid on https://github.com/mwaskom/seaborn/issues/861 by citynorman :

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
iris = load_iris()
iris = pd.DataFrame(data= np.c_[iris['data'], iris['target']],columns= iris['feature_names'] + ['target'])

g = sns.FacetGrid(iris, hue="target")
g = g.map(sns.distplot, "sepal length (cm)",  hist=False, rug=True)

Answer 4

Anyone trying to build the same plot using the new 0.11.0 version, Seaborn has or is deprecating distplot and replacing it with displot.

So the new version wise the code would be:

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'])

sns.displot(data=iris, x='sepal length (cm)', hue='target', kind='kde', fill=True, palette=sns.color_palette('bright')[:3], height=5, aspect=1.5)

Answer 5

A more recent and simpler option:

sns.displot(data=iris, x='sepal length (cm)', hue='target', kind='kde')

Answer 6

If anyone is looking to get a facetgrid of distplots or histograms in seaborn, the new sns.displot function has facetgrid built into it! This makes it quite easy to use if you melt the iris dataframe first.

Building on and updating the code in previous answers by @Abbas and @Amit Amola:

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'])
iris['target'] = iris['target'].astype(str)

iris_melt = iris.melt(id_vars='target')

iris_melt.head()

   target   variable       value
0   0.0 sepal length (cm)   5.1
1   0.0 sepal length (cm)   4.9
2   0.0 sepal length (cm)   4.7
3   0.0 sepal length (cm)   4.6
4   0.0 sepal length (cm)   5.0

sns.displot(
    data=iris_melt, 
    x='value', 
    hue='target', 
    kind='kde', 
    fill=True,
    col='variable'
)

The image is small here, but if you right click on the image and open it in a new tab or window, you can see the details better.