python for data science – Top 10 python Libraries to elevate your skills

Python is a general-purpose programming language that is useful for the analysis of data. With these 10 libraries, you can take your data science and machine learning skills to the next level.

Top 10 python Libraries for data science and machine Learning


TensorFlow is an open-source software library for numerical computation using data flow graphs. It is a symbolic math library, and it can be used to create deep neural networks. TensorFlow was originally developed by the Google Brain team for internal use, but it was released as an open-source project in November 2015. TensorFlow is a popular deep learning framework that has been used to train many of the most successful neural networks in history.


SciPy is a Python library for mathematics, science, and engineering. It provides tools for numerical computation, visualization, and programming. SciPy is a Python library for mathematics, science, and engineering. It provides tools for numerical computation, visualization, and programming. SciPy is built on NumPy and provides many user-friendly and efficient numerical routines such as routines to solve linear equations (e.g., linalg), integrate functions (e.g., integrate), optimize functions (e.g., minimize), etcetera


NumPy is a Python library that provides fast and efficient operations on arrays of homogeneous data. It is the fundamental package for scientific computing with Python. The NumPy library contains many functions to create, manipulate, and process arrays of numerical data efficiently. It also includes sophisticated linear algebra, Fourier transform, and random number capabilities.


Pandas is a Python library that provides data structures and operations for manipulating numerical tables. It is a high-level, open source tool that can be used to analyze data in many different ways. The Pandas library has been designed to work with large datasets and it can handle missing values, out-of-range values, and other types of data problems. It also has tools for merging, joining, pivoting, reshaping, aggregating and splitting datasets.


Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits.


Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. Keras provides a high-level API to develop and train deep learning models. It can run on top of either TensorFlow or Theano, two popular open-source libraries for numerical computation with deep learning models. Keras is an open source library written in Python that provides a high level neural networks API, enabling fast prototyping.


SciKit-Learn is a Python library that provides a set of tools for machine learning. It is built on top of other libraries such as NumPy, SciPy, and matplotlib. SciKit-Learn provides a range of supervised and unsupervised learning algorithms for classification, regression, clustering, dimensionality reduction and model selection. The library is designed to be used in conjunction with the Python programming language. 


PyTorch is a Python library for deep learning. It provides a high-level neural networks API to build models, train them, and make predictions.


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