Matplotlib Density Plot

  1. Generate the Density Plot Using the gaussian_kde() Method From the scipy.stats Module
  2. Generate the Density Plot Using the kdeplot() Method From the seaborn Package
  3. Generate the Density Plot Using the distplot() Method From the seaborn Package
  4. Set kind='density' in pandas.DataFrame.plot() Method to Generate the Density Plot

To generate a density plot using Python, we at first estimate the density function from the given data using the gaussian_kde() method from the scipy.stats module. We then plot the density function to generate the density plot. Alternatively, we can also use kdeplot() from the seaborn package or set kind='density' in pandas.DataFrame.plot() method to generate the density plot.

Generate the Density Plot Using the gaussian_kde() Method From the scipy.stats Module

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import kde

data = [2,3,3,4,2,1,5,6,4,3,3,3,6,4,5,4,3,2]
density = kde.gaussian_kde(data)
x = np.linspace(-2,10,300)
y=density(x)

plt.plot(x,y)
plt.title("Density Plot of the data")
plt.show()

Output:

Generate the density plot using the gaussian_kde method

Here, we first estimate the density function for the given data using the gaussian_kde() method. Then, we plot the function for values ranging from -2 to 10 using the plot() method.

The density plot generated enough is not precise, or food estimate of the data as the gaussian_kde function sets the bandwidth automatically. To set the bandwidth, we can use the covariance_factor function of the gaussian_kde class. We then call the _compute_covariance method so that all the factors are computed correctly to generate the precise plot.

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import kde

data = [2,3,3,4,2,1,5,6,4,3,3,3,6,4,5,4,3,2]
prob_density = kde.gaussian_kde(data)
prob_density.covariance_factor = lambda : .25
prob_density._compute_covariance()

x = np.linspace(-2,10,300)
y=prob_density(x)

plt.plot(x,y)
plt.title("Density Plot of the data")
plt.show()

Output:

Generate more precise density plot using the gaussian_kde method

Generate the Density Plot Using the kdeplot() Method From the seaborn Package

import matplotlib.pyplot as plt
import seaborn as sns

data = [2,3,3,4,2,1,5,6,4,3,3,3,6,4,5,4,3,2]
sns.kdeplot(data,bw=0.25)
plt.show()

Output:

Generate the density plot using the kdeplot method

In this way, we can generate the density plot by simply passing data into the kdeplot() method.

Generate the Density Plot Using the distplot() Method From the seaborn Package

We can also use the distplot() method from the seaborn package to and set hist=False to generate the density plot.

import matplotlib.pyplot as plt
import seaborn as sns

data = [2,3,3,4,2,1,5,6,4,3,3,3,6,4,5,4,3,2]
sns.distplot(data,hist=False)
plt.show()

Output:

Generate the density plot using the distplot method

Set kind='density' in pandas.DataFrame.plot() Method to Generate the Density Plot

import pandas as pd
import matplotlib.pyplot as plt

data = [2,3,3,4,2,1,5,6,4,3,3,3,6,4,5,4,3,2]
df=pd.DataFrame(data)
df.plot(kind='density')
plt.show()

Output:

Set kind=density in pandas.DataFrame.plot to generate the density plot

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