Python packages

Here are some short descriptions and links for each of the Python packages used for this script:

  1. Jupyter: Jupyter is an open-source project that allows you to create and share documents containing live code, equations, visualizations, and narrative text. These documents, called notebooks, support various programming languages, including Python, R, and Julia. Jupyter notebooks are widely used for data analysis, machine learning, scientific research, and education.

  2. Matplotlib: Matplotlib is a popular plotting library for Python that enables you to create static, interactive, and publication-quality visualizations. It provides a wide range of plotting functions for creating line plots, scatter plots, bar charts, histograms, and more. Matplotlib is highly customizable and integrates well with other Python libraries like NumPy and pandas.

  3. Plotly: Plotly is a Python graphing library that produces interactive plots and dashboards. It offers a rich set of chart types, including line charts, scatter plots, bar charts, heatmaps, and 3D plots. Plotly’s interactive features allow users to explore data dynamically, zoom in on specific regions, and add annotations. Plotly can be used both offline and online, and it integrates seamlessly with Jupyter notebooks.

  4. scikit-learn: Scikit-learn is a comprehensive machine learning library for Python that provides simple and efficient tools for data mining and analysis. It features a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn is built on top of NumPy, SciPy, and matplotlib, making it easy to integrate into existing Python workflows.

  5. rapidfuzz: RapidFuzz is a fast string matching library for Python that provides various algorithms for fuzzy string matching and string similarity calculations. It offers functions for tasks like approximate string matching, fuzzy searching, and string similarity measurements based on Levenshtein distance, Jaro distance, and cosine similarity. RapidFuzz is useful for tasks such as data deduplication, spell checking, and record linkage.

  6. NLTK: NLTK (Natural Language Toolkit) is 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, such as WordNet. NLTK includes a suite of text processing libraries for tasks like tokenization, stemming, tagging, parsing, and classification. It is widely used in education, research, and industry for natural language processing tasks.

  7. spaCy: spaCy is an open-source natural language processing library for Python that is designed for efficiency, scalability, and ease of use. It provides pre-trained models for various languages and domains, along with an easy-to-use API for tasks such as tokenization, part-of-speech tagging, named entity recognition, dependency parsing, and text classification. spaCy is known for its speed and performance, making it suitable for both research and production environments.

Tip

This script has been created with Quarto.

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