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PYTHON FRAMEWORK FOR DATA SCIENCE

A platform is a collection of modules or bundles that aid in the development of web applications. We don't have to think about low-level specifics like protocols, sockets, or thread handling while operating on frameworks in Python.

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PYTHON FRAMEWORK FOR DATA SCIENCE

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  1. PYTHON FRAMEWORK FOR DATA SCIENCE NUMPY NumPy is a Python module that adds support for huge, multi-dimensional arrays and matrices, as well as a large number of high-level mathematical functions to run on these arrays. SCIPY SciPy is a Python open-source library for solving problems in mathematics, science, engineering, and technology. It gives users the ability to control and simulate data using a variety of high- level Python commands. SciPy is based on the NumPy Python extension. "Sigh Pi" is another way of pronouncing SciPy. TENSORFLOW Google developed and published TensorFlow, a Python library for quick numerical computation. It's a base library that can be used to generate Deep Learning models directly or through wrapper libraries designed on top of TensorFlow to make the process easier. KERAS Keras is a free open source Python library for designing and testing deep learning models that is both efficient and simple to use. It wraps Theano and TensorFlow, two effective numerical computing libraries, and helps you to describe and train neural network models in just a few lines of code. MATPLOTLIB Matplotlib is a plotting library for Python and NumPy, the Python numerical mathematics extension. It offers an object-oriented API for embedding plots into applications utilising GUI toolkits such as Tkinter, wxPython, Qt, or GTK. PANDAS pandas is a data manipulation and interpretation applications library for the Python programming language. It includes data structures and operations for controlling numerical tables and time series, in particular.

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