Hey, folks! In this article, we will be focusing on **Seaborn Distplot** in detail.

Table of Contents

- 1 What is a Seaborn Distplot?
- 2 Creating a Seaborn Distplot
- 3 Adding labels to the axis of DistPlot
- 4 Seaborn DistPlot along with Kernel Density Estimate Plot
- 5 Visualizing the data with Seaborn DistPlot along with Rug Plot
- 6 Plotting Seaborn Distplot along vertical axis
- 7 Setting a different style using seaborn.set() function
- 8 Setting Custom color to Seaborn DistPlot
- 9 Conclusion
- 10 References

## What is a Seaborn Distplot?

A **Distplot **or distribution plot, depicts the variation in the data distribution. Seaborn Distplot represents the overall distribution of continuous data variables.

The Seaborn module along with the Matplotlib module is used to depict the distplot with different variations in it. The Distplot depicts the data by a histogram and a line in combination to it.

## Creating a Seaborn Distplot

Python Seaborn module contains various functions to plot the data and depict the data variations. The `seaborn.distplot() function`

is used to plot the distplot. The distplot represents the univariate distribution of data i.e. data distribution of a variable against the density distribution.

**Syntax:**

seaborn.distplot()

The seaborn.distplot() function accepts the data variable as an argument and returns the plot with the density distribution.

**Example 1:**

import numpy as np import seaborn as sn import matplotlib.pyplot as plt data = np.random.randn(200) res = sn.distplot(data) plt.show()

We have used the `numpy.random.randn() function`

to generate random data values. Further, the `pyplot.show() function`

is used show the plot.

**Output:**

**Example 2:**

import numpy as np import seaborn as sn import matplotlib.pyplot as plt import pandas as pd data_set = pd.read_csv("C:/mtcars.csv") data = pd.DataFrame(data_set['mpg']) res = sn.distplot(data) plt.show()

The `pandas.read_csv() function`

loads the dataset into the Python environment.

**Output:**

## Adding labels to the axis of DistPlot

The Seaborn Distplot can be provided with labels of the axis by converting the data values into a Pandas Series using the below syntax:

**Syntax:**

pandas.Series(data,name='name') seaborn.distplot()

Pandas Series contains a parameter ‘`name`

‘ to set the label of the data axis.

**Example:**

import numpy as np import seaborn as sn import matplotlib.pyplot as plt data = np.random.randn(200) res = pd.Series(data,name="Range") plot = sn.distplot(res) plt.show()

**Output:**

## Seaborn DistPlot along with Kernel Density Estimate Plot

The Seaborn Distplot can also be clubbed along with the** Kernel Density Estimate Plot **to estimate the probability of distribution of continuous variables across various data values.

**Syntax:**

seaborn.distplot(data,kde=True)

The `kde`

parameter is set to `True`

to enable the Kernel Density Plot along with the distplot.

**Example:**

import numpy as np import seaborn as sn import matplotlib.pyplot as plt data = np.random.randn(100) res = pd.Series(data,name="Range") plot = sn.distplot(res,kde=True) plt.show()

**Output:**

## Visualizing the data with Seaborn DistPlot along with Rug Plot

We can map the Seaborn Distplot along with Rug Plot to depict the distribution of data against bins with respect to the univariate data variable. The **Rug Plot** describes visualizes distribution of data in the form of bins.

**Syntax:**

seaborn.distplot(data, rug=True, hist=False)

The ‘`rug`

‘ parameter needs to be set to `True `

to enable the rug plot distribution.

**Example:**

import numpy as np import seaborn as sn import matplotlib.pyplot as plt data = np.random.randn(100) res = pd.Series(data,name="Range") plot = sn.distplot(res,rug=True,hist=False) plt.show()

**Output:**

## Plotting Seaborn Distplot along vertical axis

The entire Distplot can be plotted on the **y axis** using the below syntax:

**Syntax:**

seaborn.distplot(data,vertical=True)

The ‘`vertical`

‘ parameter needs to be set to `True `

to plot the distplot on the y-axis.

**Example:**

import numpy as np import seaborn as sn import matplotlib.pyplot as plt data = np.random.randn(100) plot = sn.distplot(data,vertical=True) plt.show()

**Output:**

## Setting a different style using seaborn.set() function

Seaborn has a number of in-built functions to add extra background features to the plots. The `seaborn.set() function`

is used to set different background to the distribution plots.

**Syntax:**

seaborn.set(style)

**Example**:

import numpy as np import seaborn as sn import matplotlib.pyplot as plt sn.set(style='dark',) data = np.random.randn(500) plot = sn.distplot(data) plt.show()

**Output:**

## Setting Custom color to Seaborn DistPlot

We can set different colors to the distplot to add to the visualization of the data using the ‘`color`

‘ parameter of the seaborn.distplot() function.

**Syntax:**

seaborn.distplot(data, color='color')

**Example:**

import numpy as np import seaborn as sn import matplotlib.pyplot as plt sn.set(style='dark',) data = np.random.randn(500) plot = sn.distplot(data,color='purple') plt.show()

**Output:**

## Conclusion

Thus, Seaborn Module along with Matplotlib module helps in the data visualization and depicts the distribution of data.

I strongly recommend all the readers to read the Python Matplotlib Module to understand the basics of Data Visualization.