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Python must know libraries and frameworks for Data Science

Here is a curated list of Python frameworks and libraries. I have to say, creating this I thought no one could have used them all, but after many years working with Python, I have come across all of them at some point, even if for small uses.

• NumPy - NumPy is a library for the Python programming language that adds support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

• Pandas - Pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation library for Python. It provides data structures for efficiently storing large datasets and tools for processing, cleaning, and transforming data.

• Scikit-learn - Scikit-learn is a simple and efficient tool for data mining and data analysis built on NumPy, SciPy, and matplotlib. It provides a range of supervised and unsupervised learning algorithms in Python, including classification, regression, clustering, and dimensionality reduction.

• Matplotlib - 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.

• TensorFlow - TensorFlow is an 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.

• Keras - Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation and easy-to-use interfaces for creating neural networks.

• PyTorch - PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing. It is primarily developed by Facebook's AI Research lab.

• Transformers - Transformers is a Python library that provides tools for natural language processing (NLP), particularly for working with pre-trained language models. It includes a range of state-of-the-art models for various NLP tasks, such as text classification, question-answering, and language translation. The library also provides a unified API for working with these models, making it easy to switch between different models and tasks. Transformers also includes a range of tools for fine-tuning pre-trained models on new datasets, and for optimizing model performance on specific tasks. In addition, Transformers supports a range of popular deep learning frameworks, including PyTorch and TensorFlow. Overall, Transformers is a powerful tool for working with pre-trained language models and developing NLP applications, making it well-suited for tasks such as sentiment analysis, chatbots, and language translation.

• Seaborn - Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

• OpenCV - OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products.

• XGBoost - XGBoost is an open-source software library which provides a gradient boosting framework for C++, Java, Python, R, and Julia. It is widely used for regression and classification problems.

• Statsmodels - Statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and exploratory data analysis.

• LightGBM - LightGBM is an open-source gradient boosting framework that uses tree-based learning algorithms. It is designed to be efficient and scalable, and is used for both classification and regression tasks.

• Plotly - Plotly is a web-based data visualization platform that allows users to create interactive charts, graphs, and dashboards. It is also available as a Python library that can be used to create interactive plots in Jupyter notebooks.

• SciPy - SciPy is a library for scientific computing in Python. It provides modules for optimization, integration, interpolation, eigenvalue problems, etc.

• NLTK - The Natural Language Toolkit (NLTK) is a Python library that provides tools for working with human language data. It provides tools for tokenization, stemming, tagging, parsing, and more.

• Gensim - Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. It is built on top of NumPy, SciPy, and matplotlib.

• Theano - Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is particularly well-suited for deep learning and other computationally-intensive tasks. Theano can automatically generate efficient code for GPU or CPU execution, and supports symbolic differentiation for gradient-based optimization algorithms.

• Ray - Ray is a Python library that focuses on distributed computing, providing a range of tools for scaling Python applications across multiple nodes or machines. It provides a high-level API for parallelizing Python functions and supports a range of distributed data structures and machine learning algorithms. Ray is designed to be easy to use, and it can be integrated with existing Python codebases without needing major code changes. Ray also includes features for automatic fault tolerance, so it can recover from failures without needing manual intervention. In addition to distributed computing, Ray also includes support for reinforcement learning and other machine learning algorithms. Overall, Ray is a powerful tool for scaling Python applications across multiple machines, making it well-suited for large-scale data processing and machine learning workloads.

• Joblib - Joblib is a Python library that provides tools for parallel computing within a single machine. It provides a range of tools for parallelizing Python functions across multiple CPU cores, and supports caching of intermediate results to reduce computation time. Joblib is often used for embarrassingly parallel problems, where the input data can be easily divided into independent chunks that can be processed in parallel. Joblib is designed to be easy to use, and it can be integrated with existing Python codebases without needing major code changes. Joblib also includes features for memory management, such as the ability to unload objects from memory when they are no longer needed. Overall, Joblib is a powerful tool for speeding up Python code by parallelizing computation, making it well-suited for machine learning, scientific computing, and other computationally intensive workloads.

• Prefect - Prefect is a Python library that provides tools for building, scheduling, and monitoring data workflows. It allows developers to define complex workflows as Python code, with tasks represented as Python functions that can be composed together into larger workflows. Prefect provides a range of features for managing workflows, including task-level retries, task dependencies, and automatic logging and visualization of workflow execution. Prefect also includes a dashboard for monitoring workflow execution and viewing performance metrics. Prefect is designed to be highly flexible and can be used with a range of data processing tools, including Pandas, Dask, and Apache Spark. Overall, Prefect is a powerful tool for managing complex data workflows, making it well-suited for data engineering, data science, and other data-intensive applications.

• Pygame - Pygame is a set of Python modules designed for writing video games. It includes computer graphics and sound libraries designed to be used with the Python programming language.

• Flask - Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries.

• Dash - Dash is a Python framework for building analytical web applications. It provides a set of high-level components like graphs, tables, and text boxes, and allows you to combine these components to create complex dashboards.

• Beautiful Soup - Beautiful Soup is a Python library that allows you to extract data from HTML and XML files. It provides simple ways to navigate, search, and modify the parse tree.

• SQLAlchemy - SQLAlchemy is an SQL toolkit and ORM that provides a set of high-level API for connecting to relational databases like MySQL, PostgreSQL, SQLite, and Oracle.

• Django - Django is a high-level Python web framework that enables the rapid development of secure and maintainable websites. It follows the Model-View-Controller (MVC) architectural pattern and provides a set of libraries and tools for handling common web development tasks.

• Pygame Zero - Pygame Zero is a minimal game development framework for Python. It is built on top of Pygame and provides a simple API for creating games without the need to write boilerplate code.

• FastAPI - FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use and fast to develop with.

• NetworkX - NetworkX is a Python package for the creation, manipulation, and study of complex networks. It provides tools for working with graphs and networks, including algorithms for computing network properties and visualizing graphs.

• Scrapy - Scrapy is an open-source web-crawling framework for Python. It provides a set of tools and libraries for extracting structured data from websites and APIs.

• Pandas-Profiling - Pandas-Profiling is a library for creating exploratory data analysis reports in Python. It provides a set of tools for generating statistical summaries, visualizations, and data quality checks for large datasets.

• FastText - FastText is an open-source, free, lightweight library that allows users to learn text representations and perform text classification tasks. It is developed by Facebook's AI Research team.

• Kivy - Kivy is an open-source Python library for developing multi-touch applications. It provides a set of high-level widgets for creating desktop and mobile apps, and supports a range of input methods, including touch, mouse, and keyboard.

• PySpark - PySpark is a Python API for Apache Spark, a fast and general-purpose cluster computing system. It allows you to write Spark applications using Python, and provides a set of libraries and tools for working with large datasets.

• Requests - Requests is a Python library for making HTTP requests. It provides a simple and elegant way to interact with RESTful APIs and web services.

• Pillow - Pillow is a fork of the Python Imaging Library (PIL), a library for opening, manipulating, and saving many different image file formats. It provides a set of functions for working with images, including resizing, cropping, and adding filters.

• pytest - pytest is a Python testing framework that makes it easy to write and run tests. It provides a simple and intuitive API for defining tests, and supports fixtures for setting up test data and dependencies.