In this article, we have discussed the top 10 python libraries for data visualization in 2023

Python is one of the most widely used programming languages. It serves to be a blessing in the field of data science. When one boasts of possessing good Python skills, it is expected out of that person that he/she is well acquainted with libraries in Python. Here are the top 10 Python libraries for data visualization in 2023 which make programming and developing models a lot easier.

1. SciPy

This stands for Scientific Python. This is yet another open-source library that comes in handy for all kinds of high-level computations. This plays a significant role to play in all those scientific and technical computations that you once thought weren't easy to handle. This is user-friendly and no one denies this. One of its remarkable features is its ability to solve differential equations. This library has applications in linear algebra, solving differential equations, and optimizing algorithms to name a few.

2. Gradio

This library allows you to build and deploy applications web applications. The best feature of this library is that your task is done in as little as 3 lines of code. Yet another benefit of this library that's worth a mention is how fast and easy the whole process gets. With Gradio, it is possible to test different inputs. Model validation is easier than ever with Gradio. Since there is a provision of public links, it becomes very easy to implement and distribute web applications.

3. Keras

With deep learning and neural networks becoming critical with every passing day, making use of libraries that cater to the same is the need of the hour. Here, you get vast pre-labeled datasets that serve the advantage of being imported directly and loaded. Keras has a range of implemented layers and parameters. This feature makes constructing, configuring, training, and evaluating neural networks a lot easier than one can imagine. The deep learning models resulting from Keras can be used to predict or extract features without you having to create or train the model.

4. Matplotlib

This library boasts about 26,000 comments on GitHub. The feature of this library to produce graphs and plots makes it the most sought-after library for data visualization. It is considered to be one of the best plotting libraries for Python that helps you to plot lines, scatter plots, etc. without much difficulty.

5. Orbit

This is yet another Python framework designed for Bayesian time series forecasting and inference. Its framework is built on probabilistic programming packages like PyStan and Uber's own Pyro.

6. Seaborn

It is one of those data visualization libraries that helps in drawing attractive and informative statistical graphics. Seaborn provides a high-level interface. People consider it to be an extension of Matplotlib. While Matplotlib provides a range of basic plotting features, Seaborn lets users enjoy a range of visualization patterns. Yet another feature of this library that grabs attention is that the syntax is simple and not that complex.

7. Pandas

Pandas stands for 'Python Data Analysis Library'. This is an open-source Python package that ensures delivering high performance. Here, you can find easy-to-use data structures and data analysis tools that serve to be extremely useful while programming in Python. Some of the best features of this library are:

One can plot data with a histogram or box plot.

It is very easy to add, delete and update columns.

Renaming, sorting, indexing, merging, and manipulating data frames.

8. Sktime

This is an open-source Python library exclusively designed for time series analysis. It provides an extension to the scikit-learn API for time-series solutions and contains all the required algorithms and tools that are needed for the effective resolution of time-series regression, prediction, and categorization issues.

9. Darts

Darts is yet another time series Python library that has made its way to the list of the top 10 Python libraries. Developed by Unit8, Darts is widely known for easy manipulation and forecasting of time series. It can handle large data quite well and supports both univariate and multivariate time series analysis and models.

10. Kats (Kits to Analyze Time Series)

This exceptional open-source Python library is developed by researchers at Facebook (now Meta). This time series Python library is extremely easy to use and allows one to set up the models quicker without spending much time. Additionally, it can identify patterns, seasonality, and trends.