Most Used Python Libraries For Data Analytics
Any work that is performed in the field of data science or data analytics is incomplete without the proper programming language. Programming language, in turn, is incomplete without libraries. Libraries enable you to use language to its full extent by providing different features to complete the task. The presence of libraries in programming language has increased the power of the language to a great extent. Early in the process, long and difficult codes were written to implement any task, but, now, one simply needs to use these libraries and all the work is done on its own. Python is the one language which is most commonly used in data science since it has hundreds of libraries. Below are some of the most famous and widely used libraries in Python.
NumPy
When there is a need for data-oriented aspects, this library is the first choice of all data scientists and developers. Registered under a BSD license, it is mostly used for scientific calculations and operations. Apart from this, NumPy packages can be used to maximize the usage of n-dimensional array objects and to implement functions for complex mathematical tasks such as Fourier transformation, random number, Linear Algebra, etc. Complex ML platforms are powering up their internal operations using NumPy as it is installed under TensorFlow.
Keras
This most powerful library is capable of implementing and integrating high-level neural network APIs. It was created to overcome the problems with complex research so that the operation can be computed faster. Keras is best for those who use deep learning libraries in their work. It decreases difficulties encountered in the cognitive load by delivering a user-friendly interface that is easy for any user to operate. Since it is an open source library and is written in Python, data scientists have great options to include new modules as classes and functions.
Theano
Another powerful library in the Python arsenal of the libraries which is best suited for tackling and handling multidimensional array related operations. It is similar to TensorFlow but the only drawback of it is that it is not as efficient as TensorFlow. Parallel and distributed computing can make great use of Theano. With the help of Theano, one can optimize, evaluate, and express array-related mathematical tasks. Since it is based on GPU infrastructure, it has the power to process and execute operations faster than the CPU. It is perfect for stability optimization and speed which, in turn, produces desired outcomes.
PyTorch
This is regarded as one of the largest ML libraries and is mostly used by researchers and data scientists. It assists with fast tensor computations through GPUs, dynamic graphs designs, and different complex jobs. It also plays a vital role in neural network algorithms. PyTorch is created on the basis of the machine learning library known as Torch. Over the last few years, its demand and popularity has increased because of high data-centric values.
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Other than the above-mentioned libraries, there are hundreds of other libraries used all over the world by data scientists and researchers. To gain knowledge about these amazing and powerful libraries, apply for python classes which will cover all the aspects of libraries.
