### What is Seaborn?

Seaborn is a matplotlib-based Python data visualization library. It provides a sophisticated interface for creating visually appealing and instructive statistical visuals. It has beautiful default styles, and it is also designed to work very well with Pandas data frame objects.

### Installation

To install Seaborn on your system, you have to run this code on your command line:

```
>> pip install seaborn
```

### Importing

To use `seaborn`

in our code, first, we have to import it. We will import the `seaborn`

module under the name `sns`

(the tidy way):

### Data

Now, where can we find our data to visualize? The good thing about Seaborn is that it comes with built-in data sets.

**Let's talk about some plots that help us see how a set of data is spread out. These plots are:**

###
1. **displot**

Now, the `displot`

is showing our data in histogram form. We only need to pass a single column from our data frame to `displot`

.

###
2. **jointplot**

`jointplot()`

lets you match up two `scatterplots`

for two sets of data by letting you choose wh**ich** parameter to compare:

- “scatter”
- “reg”
- “resid”
- “kde”
- “hex”

Here are different kinds of plots we can create by changing the value of the `kind`

attribute.

###
3. **pairplot**

pairplot will plot pairwise relationships across an entire data frame (for the numerical columns) and supports a color hue argument (for categorical columns). We just have to pass the data through the method.

###
4. **kdeplot**

`kdeplots`

are **Kernel Density Estimation** plots. These **KDE** plots replace every single observation with a Gaussian (Normal) distribution centered around that value.

###
**Categorical Data Plots**

###
5. B**arplot and Countplot**

B**arplot**

These extremely similar plots enable the extraction of aggregate data from a categorical feature in the data. The `barplot`

is an all-purpose plot that aggregates categorical data based on a function, by default the `mean`

. We can use an `estimator`

attribute to change the default. Here we are using standard deviation.

**Countplot**

This is essentially the same as a `barplot`

except the estimator is explicitly counting the number of occurrences. Which is why we only pass the `x`

value:

###
6. B**oxplot and Violinplot**

`Boxplots`

and `violinplots`

are two types of graphs that can be used to demonstrate the distribution of categorical data. A box plot, also known as a box-and-whisker plot, is a type of graph that displays the distribution of quantitative data in a manner that makes it easier to make comparisons between different variables or between different levels of a categorical variable. The whiskers extend to show the rest of the distribution, with the exception of points that are determined to be "outliers" using a method that is a function of the interquartile range. The box displays the quartiles of the dataset, while the whiskers show the rest of the distribution.

B**oxplot**

**Violinplot**

A violin plot plays a similar role as a box and whisker plot. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Unlike a box plot, in which all of the plot components correspond to actual data points, the violin plot features a kernel density estimation of the underlying distribution.

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