on November 22, 2019
Considering how data science world has created more job opportunities for young professionals, we decided to dedicate this space for learning more about the essence of data science projects: noteworthy Python libraries.
Building Python-powered data science models demand dipping a little deep into Python libraries for data science. At its core, any Python library hosts numerous functions, tools and methods that can help in designing a building block or accomplishing a task associated to specific types of datasets. In doing so, the use of powerful Python libraries accelerates the speed of developer’s job.
Each Python library has unique capabilities and tools to address various goals such as image/text processing, neural networks, data visualization and more. They are all used for building diverse applications like scientific research, prototyping, deep learning models, algorithms and training tools.
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We have narrated here top 10 handpicked data science Python libraries that matter in 2020 and beyond.
Used mainly in scientific fields for performing accurate numerical operations, Tensor basically provides a framework that defines numerical computations. These computing have tensors that are computational objects meant to create certain value. TensorFlow is a crucial library that has an active community of 1500 contributors and nearly 35,000 comments.
It is the blend of Numerical and Python that supports numerical computation for scientific applications. Numpy is a general-purpose array-processing package that offers high-performance multidimensional objects called arrays and tools. It eliminates the slowness problem partly by providing these multidimensional arrays as well as functions and operators that operate efficiently on these arrays. The python library NumPy has around 18,000 comments on GitHub and has glorious community of 700 contributors. For full-stack developers working on machine learning, it is mandatory to know NumPy libraries.
Like NumPy and TensorFlow, Pandas is one of the majorly preferred Python libraries for data science & analysis, most essential for the data science life cycle. The library has received around 17,000 comments on GitHub and an active community of 1,200 contributors. Its primary support is for heavy operations of intense data analysis and cleaning. It provides, fast and flexible data structures that works with structured data very quickly and intuitively.
While NumPy stands for numerical computations, SciPy is an open-source Python library for data science used heavily for high-level scientific computations. It shines with around 19,000 comments on GitHub and a reliable community of nearly 600 contributors. Extending what NumPy does, SciPy library is applied for performing various scientific calculations and technical computations.
While many Python libraries explode with great tools, functions and packages, Matplotlib provides powerful data visualizations. At its core, Matplotlib as the name implies a Python plotting library with an active community of about 700 contributors and 26,000 comments on GitHub. It is massively used for data visualizations made effective with the help of the graphs and plots it produces. Python data scientists can also rely on its object-oriented API to embed those plots into data analytics applications.
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Built after NumPy, SciPy and Matplotlib libraries, Scikit-Learn is a phenomenal Python library extensively used for data analysis and mining of complex data structures. Basically a machine learning library, it comes with handy and efficient tools that can be used to complete data analysis and mining job. Treated with best releases, Scikit-Learn is fast-growing, frequently modified Python library with much improved training methods like logistics regression and nearest neighbors.
Keras is at the moment seen as the fanciest machine learning library written in Python and offers developers coolest methods for developing neural networks. It provides facility for processing datasets, simplifying models, visualizing graphs, etc. Keras is potentially equipped to run on top of of CNTK, TensorFlow, and Theano. It is developed with a primary focus on allowing fast experimentation. It uses backend infrastructure to create computational graph and perform operations.
Seaborn is primarily a Python data visualization library that is built on top of the Matplotlib library. Seaborn is integrated with Pandas and offers a high-level interface for drawing eye-catching and informative statistical graphs. Seaborn library is focused on making data visualization a vital part of exploring and understanding data. It examines relationships among multiple variables. This library internally performs all the key semantic mapping and statistical aggregation for producing informative plots.
Intended to focus on NLP operations, NLTK is a comprehensive suite of Python libraries for Data science developers. It works as an essential natural language Toolkit that one can easily leverage to perform symbolic and statistical NLP. It includes graphical demonstrations as well as sample data. This Python library comes with a book and a cookbook to make it easier to get started with.
NLTK is embraced wholeheartedly in data science world for preparing prototyping, research systems and effective teaching and study tool.
This open-source machine learning library for Python is used for implementing network architectures like RNN, CNN, LSTM, etc. as well as other high-level algorithms. It is mainly used by researchers, business, communities of ML (Machine Learning) & AI (Artificial Intelligence). Based on Torch, it is used for applications such as natural language processing. It is developed by Facebook’s artificial-intelligence research group and “Pyro” Probabilistic programming language software of Uber is built on it.
The above top 10 powerful, highly updated Python libraries for data science developments have topped the chart for 2020 on the strength of extensive capabilities, frequency of improvements and variety of innovative tools. Also, their online comments, communities and overall popularity contribute to forming the list of these coolest Python libraries that deserve to be explored. The rise of data science and machine learning must have intrigued you to learn more about the opportunities this ever evolving and advancing field contains.
If that is the case, go on and visit some of the top online data science certification courses and training institutes to grab in-depth information. Using a single Python library may enable limited data science projects; however, for projects of enormous scope and research, developers will have to enhance their stack and cultivate broad expertise in multiple Data science libraries.