Top Python Libraries Every Data Science Professional Should Know

Top python libraries have rolled out new versions of features that could support data science applications

Top Python Libraries

Top Python Libraries

Data is the ‘Gold’ of the 21st century. It is often called the most valuable asset that businesses and even government organizations rely on. However, the inflow of data in recent years has drastically surged beyond human imagination. With so much data, businesses can do many useful things. They can analyze customer data and make informed data-driven decisions. Therefore, companies are looking for easy ways to gather and analyze data to leverage business insights. Data science professionals play a big role in handling complex data. They are in constant need of tools that could help them streamline their analytics process. As a result, python libraries emerge as the best place. For data science professionals who are seeking out advancements, then python libraries are here to help. Python libraries are easy to use and have a deep connection with data science libraries and machine learning frameworks. Recently, many top python libraries have rolled out new versions of features, supporting data science courses. IndustryWired lists the top python libraries that are frequently used by data science professionals.


Top Python Libraries for Data Science Professionals


If you are working on relational and labeled data frequently, then Pandas is the best python library for you. The Pandas package provides fast, flexible, and expressive data structures, designed to make working easy and intuitive. It is very helpful for building practical and real-world data analysis models.



To deal with large arrays or multi-dimensional matrices of data, NumPy is the best python library that comes with zero cost. Besides, NumPy also leverages axes, a multidimensional matrice and other tools to deal with linear algebra, fourier transforms, random number crunching, etc. The python library also carries out other tasks like adding, slicing, multiplying, flattening, reshaping, and indexing the arrays in an easy way. 



Scikit-learn is another free python library designed specifically for machine learning coding works. Initially developed as a Google Summer of Code project by David Cournapeau and released in 2007, Scikit-learn is developed over other python libraries to streamline interoperability. Besides Python, Scikit-learn also addresses some of Cython algorithms in its performance.



Matplotlib houses and addresses the most important step in data analytics called numerical plotting. It also acts as a 2D numerical plotting library for data science professionals. With the help of Matplotlib, various types of plots like histograms, power spectra, scatterplots, error charts, etc can be created. 



Normal array operations are very easy and data professionals can do it without the help of an external library. However, Multidimensional array operations are quite hectic and need support from the python library. Theono comes as a handy solution to address distributed and parallel computing issues. Oftentimes, Theono is used alongside NumPy to allow multidimensional array operations. 



Unlike other python libraries with basic functionalities, Keras allows the integration of high-level APIs. It reduces the challenges faced in complex research by allowing data science professionals to compute faster. If you are a data scientist who is well-versed in handling deep learning libraries, then Keras is for you. Keras allows fast-prototyping, supports recurrent and convolution networks individually, and aids the execution over GPU and CPU. 



Designed by Google, Tensorflow is an open-source library that supports data low graphs with empowered machine learning algorithms. It helps in training neural networks to function well. One of the biggest advantage of Tensorflow is that the library is not limited only to scientific computations performed by Google, but is widely useful in developing real-world applications. 


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