# How to Plot a Distribution in Seaborn (With Examples)

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.