You can use the following methods to plot a distribution of values in Python using the seaborn data visualization library:
Method 1: Plot Distribution Using Histogram
sns.displot(data)
Method 2: Plot Distribution Using Density Curve
sns.displot(data, kind='kde')
Method 3: Plot Distribution Using Histogram & Density Curve
sns.displot(data, kde=True)
The following examples show how to use each method in practice.
Example 1: Plot Distribution Using Histogram
The following code shows how to plot the distribution of values in a NumPy array using the displot() function in seaborn:
import seaborn as sns
import numpy as np
#make this example reproducible
np.random.seed(1)
#create array of 1000 values that follow a normal distribution with mean of 10
data = np.random.normal(size=1000, loc=10)
#create histogram to visualize distribution of values
sns.displot(data)
The x-axis displays the values in the distribution and the y-axis displays the count of each value.
To change the number of bins used in the histogram, you can specify a number using the bins argument:
import seaborn as sns
import numpy as np
#make this example reproducible
np.random.seed(1)
#create array of 1000 values that follow a normal distribution with mean of 10
data = np.random.normal(size=1000, loc=10)
#create histogram using 10 bins
sns.displot(data, bins=10) 
Example 2: Plot Distribution Using Density Curve
The following code shows how to plot the distribution of values in a NumPy array using a density curve:
import seaborn as sns
import numpy as np
#make this example reproducible
np.random.seed(1)
#create array of 1000 values that follow a normal distribution with mean of 10
data = np.random.normal(size=1000, loc=10)
#create density curve to visualize distribution of values
sns.displot(data, kind='kde')
The x-axis displays the values in the distribution and the y-axis displays the relative frequency of each value.
Note that kind=’kde’ tells seaborn to use kernel density estimation, which produces a smooth curve that summarizes the distribution of values for a variable.
Example 3: Plot Distribution Using Histogram & Density Curve
The following code shows how to plot the distribution of values in a NumPy array using a histogram with a density curve overlaid:
import seaborn as sns
import numpy as np
#make this example reproducible
np.random.seed(1)
#create array of 1000 values that follow a normal distribution with mean of 10
data = np.random.normal(size=1000, loc=10)
#create histogram with density curve overlaid to visualize distribution of values
sns.displot(data, kde=True)
The result is a histogram with a density curve overlaid.
Note: You can find the complete documentation for the seaborn displot() function here.
Additional Resources
The following tutorials explain how to perform other common tasks using seaborn:
How to Add a Title to Seaborn Plots
How to Change Font Size in Seaborn Plots
How to Adjust Number of Ticks in Seaborn Plots