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Seaborn

Home » Software Development » Python Tutorials » Seaborn

Seaborn

Definition of Seaborn

Seaborn in python issued to create graphics which is easy to manage. Seaborn is a library provided by python, which basically helps to visualize the data and make it more and more undertakable by the user. With the help of the library, we can plot our data and make a graphical representation of it. Internally this library uses matplotlib; in short, it is based on matplotlib only. This also makes it efficient to create attractive and more informative graphics representations of our data. This library is integrated with the panda’s data structure. In the coming section of the tutorial, we will see how to use a seaborn library to make interactive graphics and understand its working in detail.

Why Do We Need Seaborn?

We always have a large amount of data, or we may have some applications which deal with the large data, so in order to represent our data in a good way, we may require some library which can be able to represent our data that is begin stored in a table, array, list, and other data structure efficiently. So seaborn is the library which can represent our data stored in an array, list o any other data structure in a graphical form, which helps users and developers to get more clarity of data if we need to handle large data in our application. So in order to visualize our data, we can use the seaborn library from python; it is based on matplotlib only. This can be used for model fitting and data representation etc. This library is data-oriented, but we need to install it before using it. In the coming section, we will explore more about internal working.

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How does Seaborn Works?

Seaborn is the python library that is used to create the plot and graphical representation of the data, internally it maps our data and create an informative plot of the data, which is easy to understand the data. Let’s take a look at what steps needs to be taken in order to use the seaborn library in our program; see below;

First, we need to install the seaborn library in order to use it; follow the below commands to install it

  1. pip install seaborn

When we install this library, it will also install some of the basic library for us, which are mentioned below;

  • scipy
  • matplotlib
  • numpy
  • pandas

If you want to use this in your program, then we have to import this while programming;

import seaborn as sns As you can see in the above line of syntax, we are using a simple import statement followed by the library name; we can give the alias name to use this further. It provide us with various methods to handle our graphics and design graphics to show the data; we will see one sample example of how to create this;

  • import: First, import this into the program. using below statement;
  • import seaborn as sns
  • set_theme(): This method is provided by the seaborn library only, which is used to set the default them for the plot.
  • dataset: After this used to load the dataset. These datasets are nothing but the data frames from pads as It is very flexible in accepting data structure. The method used to load the dataset is; ‘load_dataset()’; inside this, you can pass your dataset.
  • relplot: This method is provided by the seaborn library, which is used to create the visualization of data over the plot. Inside this, we can pass our data, name for x and y-axis, style, size, hue, and many more attributes. We can call this on the seaborn library alias name, which we used at the first step. Let’s take a look at its syntax in detail see below;

Example:

seaborn.relplot(
data="your data",
x="name for x axis", y="name for y axis",
hue="your_hue", style="your_style", size="and size",
)

We have one more important property of relplot() function, which is ‘kind’; this property lets us easily switched between the different representation style line, dot, etc. In the coming section of the tutorial, we will see one practice example to understand its implementation and usage in detail.

Advantages

We have some advantages of using seaborn in our application which is as follows;

  • By using the seaborn library, we can easily represent our data on a plot.
  • This library is used to visualize our data; we do not need to take care of the internal details; we just have to pass our data set or data inside the relplot() function, and it will calculate and place the value accordingly.
  • Inside this, we can switch to any other representation of data using the ‘kind’ property inside it.
  • It creates an interactive and informative plot to representation our data; also, this is easy for the user to understand and visualize the records on the application.
  • It uses static aggregation for plot generation in python.
  • As it is based on the matplotlib so while installing seaborn, we also have other libraries installed, out of which we have matplotlib, which also provides several features and functions to create more interactive plots in python.

Disadvantages

Some of the disadvantages are:

  • There are no such disadvantages of using the seaborn library; one thing that we have to do is its installation because it is not in built in python. we require to install this before use by using some commands. Apart from this, it does not have any disadvantages, it is also not a disadvantage, but a step added to use this while programming.

Usage of Seaborn

Usage of seaborn are given below:

  • Data visualization
  • Use to show data as a line plot.
  • Use to create high level informative and attractive plots to show the data.

Conclusion

By we have already discussed that it is basically used for data visualization because, in our application, we have a large amount of data that needs to be represented to the user in some readable and undertakable format, so seaborn helps the user to visualize data in the form of graphics. which makes it attractive and efficient.

Recommended Articles

This is a guide to Seaborn. Here we also discuss the definition and how seaborn works? along with advantages and disadvantages. you may also have a look at the following articles to learn more –

  1. Pandas Set Index
  2. What can I do with Python?
  3. Logistic Regression in Python
  4. NumPy Histogram

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