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Top 15 Python Libraries For Data Science & Best Tutorials To Learn Them


Python is the most widely used programming language today. When it comes to solving data science tasks and challenges, Python never ceases to surprise its users. Most data scientists are already leveraging the power of Python programming every day. 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 has been built with extraordinary Python libraries for data science that are used by programmers every day in solving problems.Here today, We have curated a list of best 15 Python libraries that helps in Data Science and its periphery, when to use them, their advantages and best tutorials to learn them. Pandas stand for Python Data Analysis Library. Pandas is the best tool for data wrangling or munging.

Applications of Python


Python is a simple, open-source and object-oriented coding language. It is one of the programming languages that are easy to learn as it is a dynamic type, high-level, and interpreted coding language. This is also used for debugging of errors and motivate for instant growth of application prototypes and using it as a language to program with. Python programming language was originated by Guido Van Rossum in 1989 which is based on the DRY (Do not Repeat Yourself) principle. This blog will provide you the various uses of Python that help you to understand where one can easily implement the Python programming language and execute it in different sectors.

Top 30 Python Libraries for Machine Learning


In this article, you'll see top 30 Python libraries for Machine Learning. In this article, you'll see top 30 Python libraries for Machine Learning. Today, Python is one of the most popular programming languages and it has replaced many languages in the industry. There are various reasons for its popularity and one of them is that python has a large collection of libraries. Python is one of the most widely used languages by Data Scientists and Machine Learning experts across the world. Though there is no shortage of alternatives in the form of languages like R, Julia and others, python has steadily and rightfully gained popularity. Similar to the Google Trends shown above(the plot is prepared using matplotlib and pytrends), confidence is visible year over year with python featuring way above its peers in the StackOverflow surveys for 2017 and 2018. These trends/surveys are the consequences of ease of use, shorter learning curve, widespread usage, strong community, large number of libraries covering depth and breadth of a number of research and application areas. The amazing popularity might make one think that python is the gold standard for Machine Learning.

Python Packages for Data Science - DZone Big Data


Python is one of the most widely used programming languages. Although standard Python does not offer too much, its insane number of open-source and third-party libraries holding its popularity amongst the developers. You just name the domain and Python will provide you with its best packages and libraries. Data Science and Machine Learning are two demanding technologies of this era, and Python is doing better than excellent in these two fields. Apart from Python, R is another programming language that often used in Data Science projects. R is faster and contains more computational and statistical libraries; however, in this article, we have only covered the top Python Data Science Libraries which you should know if you want to master Data Science.

Your one-stop to all machine learning libraries


In the previous blogs of the series "Complete Machine Learning and Deep Learning for Beginners," we have introduced you to the logical and mathematical concepts of machine learning. But moving forward, our blogs will be more coding-oriented, which requires a brief introduction of what libraries are used in machine learning. The library has always been a repository for knowledge and research tools. With the advent of big data and data science, research has become more powerful and data-driven in recent years. In the world of data science, "Libraries" work in the same manner as they do in the real world. Libraries help data scientists conceptualize how data is collected, organized, and stored.