Always a selection of useful and best libraries for our goals can be so effective. About data science, the most important Python libraries for data science tasks covering areas: data mining, data processing, modeling, and visualization are:
Python libraries for Data Mining or Data discovery
Scrapy is a free and open-source web-crawling framework written in Python. Originally designed for web scraping, it can also be used to extract data using APIs or as a general-purpose web crawler.
Beautiful Soup is a Python package for parsing HTML and XML documents. It creates a parse tree for parsed pages that can be used to extract data from HTML, which is useful for web scraping.
Python libraries for Data Processing and Statistics Modeling
NumPy (Numerical Python) is a perfect tool for scientific computing and performing basic and advanced array operations.
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
SciPy is a free and open-source Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, and other tasks common in science and engineering.
pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.
Keras is an open-source library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library. Up until version 2.3 Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, R, Theano, and PlaidML.
Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression, and clustering algorithms including support vector machines, …
PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab. It is free and open-source software released under the Modified BSD license.
TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks.
XGBoost is an open-source software library that provides a gradient boosting framework for C++, Java, Python, R, Julia, Perl, and Scala. It works on Linux, Windows, and macOS. From the project description, it aims to provide a “Scalable, Portable and Distributed Gradient Boosting Library”.
Python libraries for Data Visualization
Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+.
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords …
plotly.py is an interactive, open-source, and browser-based graphing library for Python
This library helps to generate oriented and non-oriented graphs. It serves as an interface to Graphviz (written in pure Python). You can easily show the str ucture of graphs with the help of this library. That comes in handy when you’re developing algorithms based on neural networks and decision trees.
There are many other tools that can be helpful for data science work. These libraries are essential for building high-performing ML models in Python.
Python → Libraries → Modules
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