Python is the most popular and commonly used programming language today. Pythons’ importance and relevance in the programming world cannot be understated and this is even more so when it comes to data science. Data scientists are using python to carry out high-order tasks and to deliver key business insights. Python is an easy-to-learn, easy-to-debug, widely used, object-oriented, open-source, high-performance language, and there are many more benefits to Python programming. Python is built on astounding python libraries that are used by data scientists to solve problems and reach conclusions. These python libraries are an integral part of any well-curated data science course syllabus. Here are the top python libraries for data science.
5 Python Libraries for Data Science
Tensor flow is a python library for data science that is comprised of over 35,000 comments and is run by an active community of over 1500 contributors. It is a library for high-performance numerical computations and is used across various scientific fields. Tensor flow has various advantages such as reducing error by 50 to 60 percent in neural machine learning, parallel computing to execute complex models, and a seamless library backed by Google.
Numerical Python aka NumPy is a fundamental library that contains a powerful N-dimensional array object. It has a strong community of over 700 active contributors and over 18000 comments on GitHub. NumPy provides fast, precompiled functions for numerical tasks and supports an object-oriented approach. NumPy is a python library for data science that is used in data analysis and as a replacement for MATLAB when it is used with SciPy and matplotlib.
SciPy is used for high-level computations and is a python library for data science that is free and open source. It has an active community of over 600 contributors constantly backing and has over 19000 comments on GitHub. SciPy acts as an extension of NumPY and is extensively used for scientific and technical computations. It is used in linear algebra, multidimensional image operations, and optimization of algorithms. It also includes built-in functions for solving differential equations.
Matplotlib is a python library for data science that is primarily used for plotting in python. It is backed by a very active community of 700 contributors and has over 26000 comments on GitHub. Matplotlib is known for the graphs and plots that it produces and hence is extensively used for data visualization. It can be used as a MATLAB replacement, with the added benefit of being free and open source. It has Low memory consumption and better runtime behavior and supports dozens of backends and output types.
Python data analysis aka Pandas is the most popular and widely used Python library for data science. It has an active community of over 1200 contributors and has over 17000 comments on GitHub. It is heavily used in data cleaning and analysis. Panda is known to provide fast, flexible data structures, such as data frame CDs, which are designed to work with structured data seamlessly and intuitively. It comes with high-level abstraction and contains high-level data structures and manipulation tools. It is used in ETL jobs for data transformation and data storage and also in time series-specific functionalities such as moving window and date shifting.
Python is indeed an integral part of the arsenal that a data scientist must possess. And having thorough knowledge and expertise with the above-mentioned python libraries for data science will prove to be invaluable when you kickstart your career as a data scientist. We at Praxis understand what it takes to develop the next generation of proficient data scientists, and are proud to present our state-of-the-art Post-Graduate Program in Data Science. This program is curated to help you get all the insights and tools you need to become a remarkable data scientist and to make your mark in the industry.