Top Python libraries for Data Science that you need to know

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 one is well acquainted with libraries in Python. Here are the top Python libraries for data science that you need to know so that programming and developing models becomes a lot easier.

• 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 there's no one who'd deny this. One of the 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.
• Gradio – This library allows you to build and deploy 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 link, it becomes very easy to implement and distribute the web applications.
• 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-labelled 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 evaluation of neural networks a lot easier than one can imagine. The deep learning models resulting out of Keras can be used to predict or extract features without you having to create or train the model.
• Matplotlib – This library boasts of 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 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.
• 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 Matplot While Matplotlib provides a range of basic plotting features, Seaborn lets the 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.
• Pandas – Pandas stands for 'Python Data Analysis Library'. This is an open-source Python package that ensures in 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 –

1. One can plot data with histogram or box plot.
2. It is very easy to add, delete and update columns.
3. Renaming, sorting, indexing, merging and manipulating data