Overview of Python Libraries

Libraries are a set of pre-built functions and algorithms that makes our work easy and simple. And if you’re still wondering what makes Python so crucial and versatile for data science, then it’s none-other than libraries. Python has multiple libraries to its name and one of the most valid reasons for the flourishing data-driven technology. 

The different libraries in Python are: 

NumPy – Numpy or numerical Python is a perfect tool for scientific computing and performing the array option from the basic to the advanced version. The NumPy has many handy functionalities and features for an array and matrices in Python. It helps the process to store data of the array and at the same time, let it operate. NumPy increases the mathematical operation and boosts the execution time through performances. 

SciPy – It has all the advanced features to its name that includes linear algebra, optimization, integration, and statistics. This SciPy is built upon NumPy to make the most use of the array.  The SciPy works the best for scientific programming projects based on science math and engineering works the best for numerical optimization, integration, and different sub-modules with advanced features and functionalities.  

Pandas – pandas is a library for developers to work with labeled and relational data intuitively. Pandas mainly focus on series (one dimensional) and data frames (tables with multiple rows and columns). Pandas reduce the complexity of the program by handling missing data, data frame objects, formatting the rows and columns, and the data wrangling and visualization process.  

Scikit-learn – this is the most favorite python library for data scientists and analysts. Scikit is a group of the package. In addition to SciPy, it offers specific functionalities mostly for a mathematical function. And to provide machine learning functionalities to train algorithms and implement for better results. Data scientists and machine learning engineers use standard machine learning codes such as clustering, selecting appropriate models. Dimension reduction while maintaining high performance. 

Matplotlib – it is one of the favorite libraries for data scientists, business analysts, and data analysts as it deals with two-dimensional figures. It’s one of the best ways to analyze data visually. Matplotlib is used in data science projects for generating advanced visualizations. 

Seaborn – it is based upon Matplotlib and one of the most used python tools for visualizing statistical models. Heatmaps and other data visualization tools summarize the entire data, and they serve as an extensive gallery of data through series, plots, and others.

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