Last updated on Dec 21, 2022216748<\/p>\n
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Top 10 Python Libraries for Data Science<\/a><\/p>\n 1.TensorFlow<\/a><\/p>\n 2. SciPy<\/a><\/p>\n 3. NumPy<\/a><\/p>\n 4. Pandas<\/a><\/p>\n View More<\/p>\n 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<\/a> are already leveraging the power of Python programming every day. Python<\/a> 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<\/a>. Python has been built with extraordinary Python libraries for data science that are used by programmers every day in solving problems. Here\u2019s the top 10 Python libraries for data science:<\/p>\n The first in the list of python libraries for data science<\/a> is TensorFlow. TensorFlow<\/a> is a library for high-performance numerical computations with around 35,000 comments and a vibrant community of around 1,500 contributors. It\u2019s used across various scientific fields. TensorFlow is basically a framework for defining and running computations that involve tensors, which are partially defined computational objects that eventually produce a value.<\/p>\n TensorFlow is particularly useful for the following applications:<\/p>\n In Collaboration with IBMEXPLORE COURSE<\/a><\/p>\n SciPy (Scientific Python) is another free and open-source Python library for data science that is extensively used for high-level computations. SciPy has around 19,000 comments on GitHub<\/a> and an active community of about 600 contributors. It\u2019s extensively used for scientific and technical computations, because it extends NumPy<\/a> and provides many user-friendly and efficient routines for scientific calculations.<\/p>\n NumPy (Numerical Python)<\/a> is the fundamental package for numerical computation in Python; it contains a powerful N-dimensional array object. It has around 18,000 comments on GitHub and an active community of 700 contributors. It\u2019s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays. <\/p>\n Pandas (Python data analysis)<\/a> is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. With around 17,00 comments on GitHub and an active community of 1,200 contributors, it is heavily used for data analysis and cleaning. Pandas provides fast, flexible data structures, such as data frame CDs, which are designed to work with structured data very easily and intuitively. <\/p>\n Also Read: What is Data Analysis: Methods, Process and Types Explained<\/a><\/p>\n Python Students Also Learn<\/p>\n Data Science<\/a> | Machine Learning<\/a> | Tableau<\/a> | Data Analysis<\/a> | Statistics<\/a><\/p>\n JavaScript<\/a> | Excel<\/a> | Deep Learning<\/a> | Artificial Intelligence<\/a><\/p>\n Matplotlib<\/a> has powerful yet beautiful visualizations. It\u2019s a plotting library for Python with around 26,000 comments on GitHub and a very vibrant community of about 700 contributors. Because of the graphs and plots that it produces, it\u2019s extensively used for data visualization. It also provides an object-oriented API, which can be used to embed those plots into applications. <\/p>\n Also Read: Exploring The Data Science Learning Path<\/a><\/p>\n Build your career in Data Analytics with our Data Analyst Master’s Program<\/a>! Cover core topics and important concepts to help you get started the right way!<\/p>\n Similar to TensorFlow, Keras is another popular library that is used extensively for deep learning and neural network modules. Keras supports both the TensorFlow and Theano backends, so it is a good option if you don\u2019t want to dive into the details of TensorFlow.<\/p>\n Also Read: Keras vs Tensorflow vs Pytorch<\/a><\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Top 10 Python Libraries for Data Science for 2023 By Nikita Duggal Last updated on Dec 21, 2022216748 Table of Contents Top 10 Python Libraries for Data Science 1.TensorFlow 2. SciPy 3. NumPy 4. Pandas View More Python is the most widely used programming language today. When it comes to solving data science tasks and… Continue reading Top 10 Python Libraries for Data Science for 2023<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":113,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-612","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","entry"],"_links":{"self":[{"href":"https:\/\/sparkradius.in\/index.php\/wp-json\/wp\/v2\/posts\/612","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sparkradius.in\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sparkradius.in\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sparkradius.in\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sparkradius.in\/index.php\/wp-json\/wp\/v2\/comments?post=612"}],"version-history":[{"count":0,"href":"https:\/\/sparkradius.in\/index.php\/wp-json\/wp\/v2\/posts\/612\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sparkradius.in\/index.php\/wp-json\/wp\/v2\/media\/113"}],"wp:attachment":[{"href":"https:\/\/sparkradius.in\/index.php\/wp-json\/wp\/v2\/media?parent=612"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sparkradius.in\/index.php\/wp-json\/wp\/v2\/categories?post=612"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sparkradius.in\/index.php\/wp-json\/wp\/v2\/tags?post=612"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Top 10 Python Libraries for Data Science<\/h2>\n
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1.TensorFlow<\/h2>\n
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2. SciPy<\/h2>\n
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3. NumPy<\/h2>\n
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4. Pandas<\/h2>\n
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5. Matplotlib<\/h2>\n
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6. Keras<\/h2>\n
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