Which python libraries are commonly used

NumPy: Essential for numerical computing in Python, providing support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions.
Pandas: Widely used for data manipulation and analysis, particularly for structured data. It provides powerful data structures like DataFrames and Series, along with tools for reading and writing data from various file formats.
Matplotlib: A versatile plotting library that enables the creation of static, animated, and interactive visualizations in Python. It’s often used for creating charts, graphs, histograms, and more.
Seaborn: Built on top of Matplotlib, Seaborn provides a high-level interface for creating attractive and informative statistical graphics. It simplifies the process of visualizing complex datasets and is commonly used for data exploration and presentation.
Scikit-learn: A comprehensive machine learning library that offers simple and efficient tools for data mining and data analysis. It provides support for various machine learning algorithms, including classification, regression, clustering, dimensionality reduction, and more.
TensorFlow / PyTorch: These deep learning frameworks are widely used for building and training neural networks. TensorFlow is developed by Google, while PyTorch is maintained by Facebook. Both frameworks offer extensive capabilities for deep learning research and production deployment.
Keras: While Keras can run on top of TensorFlow or other deep learning libraries, it provides a high-level neural networks API that simplifies the process of building and training deep learning models. It’s known for its user-friendly interface and flexibility.
NLTK (Natural Language Toolkit): A leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and more.