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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=â€™kdeâ€™** 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