# Seaborn

Numpy and Seaborn are two powerful Python libraries that provide a wide range of statistical and visualization features.

This article provides examples of Numpy random seaborn to demonstrate its benefit in development and we will explore the intersection between these two libraries and how we can use them together to analyze and visualize data.

Table Of Content-

## Seaborn Visualizes Distributions

Seaborn is a library that creates graphs through Matplotlib on the backend.

It provides a high-level interface for creating informative and visually appealing statistical graphics.

Random distributions will be displayed through it.

### Using Numpy Random with Sea-born

One of the main benefits of using Numpy Random with Sea-born is that we can create random datasets with Numpy and visualize them with Sea-born.

Let’s create a random dataset using Numpy and visualize it with Sea-born:

### Install Seaborn.

In case you happen to have Python and PIP installed on your system, you can install sea-born by running the following command:

`C:\Users\Your Name>pip install seaborn`

You can install Sea-born by executing the following command if you are working with Jupyter:

`C:\Users\Your Name>!pip install seaborn`

### Distplots

The term “distplot” stands for a distribution plot, which takes an array of discrete data as input and then generates a curve corresponding to the distribution of the discrete data elements that are actually displayed in the array.

### Import Matplotlib

In addition to Numpy random sea-born, utilize the following statement to import the pyplot object of Matplotlib:

```import matplotlib.pyplot as pt
```

### Import Seaborn

Utilize the below statement to import the Sea-born module into your code:

```import seaborn as sbn
```

## Displaying a Plot

let’s create a random dataset drawn from a Poisson distribution and plot it with Sea-born:

#### Example:

import matplotlib.pyplot as pt import seaborn as sbn sbn.distplot([0, 1, 2, 3,]) pt.show()

### Example Explanation

In above example, First we have import the required modules matplotlib.pyplot and sea-born, then uses the seaborn.distplot() function to create a visualization of the distribution of the data in the list [0, 1, 2, 3].

The distplot() function creates a histogram of the data, along with an estimate of the probability density function using a kernel density estimate (KDE).

The distplot() function is a high-level interface for creating visualizations of univariate distributions.

By default, it shows both a histogram and a KDE plot, as well as some summary statistics.

In this case, since there are only four data points, the histogram is not particularly informative, but the KDE plot still provides some useful information about the distribution of the data.

Finally, the pt.show() function is used to display the plot. This function is required to render the plot and display it in a separate window or notebook cell.

#### Example:

import matplotlib.pyplot as pt import seaborn as sbn sbn.distplot([10, 20, 30, 40, 50]) pt.show()

### Plotting a Distplot Without the Histogram

To remove the histogram from the distplot, we can set the hist parameter to False:

#### Example:

import matplotlib.pyplot as pt import seaborn as sbn sbn.distplot([0, 1, 2, 3], hist=False) pt.show()

### Example Explanation

In above example, we have create a visualization of the distribution of the data in the list [0, 1, 2, 3]. However, unlike the previous example, the hist parameter is set to False to remove the histogram from the plot.

As a result, the resulting plot shows only the kernel density estimate (KDE) of the data.

This can be useful in situations where the histogram is not informative or visually appealing, and we only want to focus on the shape of the KDE.

Finally, the pt.show() function is used to display the plot.

#### Example:

import matplotlib.pyplot as pt import seaborn as sbn sbn.distplot([10, 20, 30, 40, 50], hist=False) pt.show()

### Conclusion

In this article, we explored how to create a distplot without the histogram using hist parameter.

We also saw how to customize the appearance of the kernel density estimate using the kde_kws parameter.

By removing the histogram from the distplot, we can focus solely on the shape of the kernel density estimate, which can be useful in certain data analysis scenarios.

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