Enables you to create your own function and run it across a series of data.Eloquent syntax and rich functionalities that gives you the freedom to deal with missing data.Pandas provides fast, flexible data structures, such as data frame CDs, which are designed to work with structured data very easily and intuitively.Īlso Read: What is Data Analysis: Methods, Process and Types Explained Features: With around 17,00 comments on GitHub and an active community of 1,200 contributors, it is heavily used for data analysis and cleaning. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. Pandas (Python data analysis) is a must in the data science life cycle. Replacement of MATLAB when used with SciPy and matplotlib.Forms the base of other libraries, such as SciPy and scikit-learn.Compact and faster computations with vectorization.Array-oriented computing for better efficiency.Provides fast, precompiled functions for numerical routines.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. It’s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. It has around 18,000 comments on GitHub and an active community of 700 contributors. NumPy (Numerical Python) is the fundamental package for numerical computation in Python it contains a powerful N-dimensional array object.
Solving differential equations and the Fourier transform.
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SciPy (Scientific Python) is another free and open-source Python library for data science that is extensively used for high-level computations. TensorFlow is particularly useful for the following applications: Quicker updates and frequent new releases to provide you with the latest features.Seamless library management backed by Google.Parallel computing to execute complex models.Reduces error by 50 to 60 percent in neural machine learning.Better computational graph visualizations.TensorFlow is basically a framework for defining and running computations that involve tensors, which are partially defined computational objects that eventually produce a value. It’s used across various scientific fields. TensorFlow is a library for high-performance numerical computations with around 35,000 comments and a vibrant community of around 1,500 contributors. The first in the list of python libraries for data science is TensorFlow.
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