[python] How to create a density plot in matplotlib?

In R I can create the desired output by doing:

data = c(rep(1.5, 7), rep(2.5, 2), rep(3.5, 8),
         rep(4.5, 3), rep(5.5, 1), rep(6.5, 8))
plot(density(data, bw=0.5))

In python (with matplotlib) the closest I got was with a simple histogram:

import matplotlib.pyplot as plt
data = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8
plt.hist(data, bins=6)
plt.show()

I also tried the normed=True parameter but couldn't get anything other than trying to fit a gaussian to the histogram.

My latest attempts were around scipy.stats and gaussian_kde, following examples on the web, but I've been unsuccessful so far.

The density plot can also be created by using matplotlib: The function plt.hist(data) returns the y and x values necessary for the density plot (see the documentation https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.hist.html). Resultingly, the following code creates a density plot by using the matplotlib library:

import matplotlib.pyplot as plt
dat=[-1,2,1,4,-5,3,6,1,2,1,2,5,6,5,6,2,2,2]
a=plt.hist(dat,density=True)
plt.close()
plt.figure()
plt.plot(a[1][1:],a[0])      

This code returns the following density plot

You can do something like:

s = np.random.normal(2, 3, 1000)
import matplotlib.pyplot as plt
count, bins, ignored = plt.hist(s, 30, density=True)
plt.plot(bins, 1/(3 * np.sqrt(2 * np.pi)) * np.exp( - (bins - 2)**2 / (2 * 3**2) ), 
linewidth=2, color='r')
plt.show()

Option 1:

Use pandas dataframe plot (built on top of matplotlib):

import pandas as pd
data = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8
pd.DataFrame(data).plot(kind='density') # or pd.Series()

Option 2:

Use distplot of seaborn:

import seaborn as sns
data = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8
sns.distplot(data, hist=False)

Five years later, when I Google "how to create a kernel density plot using python", this thread still shows up at the top!

Today, a much easier way to do this is to use seaborn, a package that provides many convenient plotting functions and good style management.

import numpy as np
import seaborn as sns
data = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8
sns.set_style('whitegrid')
sns.kdeplot(np.array(data), bw=0.5)

Maybe try something like:

import matplotlib.pyplot as plt
import numpy
from scipy import stats
data = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8
density = stats.kde.gaussian_kde(data)
x = numpy.arange(0., 8, .1)
plt.plot(x, density(x))
plt.show()

You can easily replace gaussian_kde() by a different kernel density estimate.