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  • Beautiful Soup
  • Geopandas
  • Matplotlib
  • NLTK (Natural Language Toolkit)
  • NumPy
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  1. Disciplines

๐ŸŒ Environmental Law and Policy

Previous๐Ÿ“š EducationNext๐ŸŒฟ Environmental Science

Last updated 1 year ago

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Beautiful Soup

  • Description: A library for pulling data out of HTML and XML files.

  • Use Case: Used for web scraping to gather environmental regulations, policies, and legal documents from various online sources.

  • Documentation:

  • GitHub Repository:

Geopandas

  • Description: An open-source project to make working with geospatial data in Python easier.

  • Use Case: Handling geographical and spatial data, crucial for environmental policy analysis, such as mapping protected areas or analyzing environmental impact.

  • Documentation:

  • GitHub Repository:

Matplotlib

  • Description: A library for creating static, animated, and interactive visualizations in Python.

  • Use Case: Visualizing environmental data, creating charts and graphs for policy documents and reports.

  • Documentation:

  • GitHub Repository:

NLTK (Natural Language Toolkit)

  • Description: A leading platform for building Python programs to work with human language data.

  • Use Case: Text analysis in environmental law and policy research, such as analyzing legal documents and policy papers.

NumPy

  • Description: Fundamental package for scientific computing with Python.

  • Use Case: Basic numerical and statistical operations essential for environmental data analysis.

Pandas

  • Description: Data analysis and manipulation library.

  • Use Case: Organizing, analyzing, and manipulating datasets related to environmental law and policy.

Plotly

  • Description: An interactive graphing library.

  • Use Case: Creating interactive plots and visualizations to represent environmental data and policy impacts.

PyPDF

  • Description: A library for working with PDF files in Python.

  • Use Case: Extracting text from PDF documents like legal texts, policy papers, and environmental reports for analysis.

Scikit-learn

  • Description: Machine learning in Python.

  • Use Case: Predictive modeling and analysis of environmental data, such as impact assessments and compliance monitoring.

Seaborn

  • Description: A Python data visualization library based on Matplotlib.

  • Use Case: Creating statistical graphics that can provide insights into environmental trends and policy effectiveness.

spaCy

  • Description: An open-source software library for advanced natural language processing.

  • Use Case: Analyzing and processing large volumes of text in environmental policy documents and legal texts.

TextBlob

  • Description: A library for processing textual data, providing simple APIs for common natural language processing tasks.

  • Use Case: Sentiment analysis, part-of-speech tagging, and classification of text, useful in analyzing public opinions on environmental issues.

WordCloud

  • Description: A tool for creating image word clouds from text.

  • Use Case: Visualizing the most prominent words in environmental policy documents, speeches, and discussions.

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GitHub Repository:

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Beautiful Soup Documentation
Beautiful Soup GitHub
GeoPandas Documentation
GeoPandas GitHub
Matplotlib Documentation
Matplotlib GitHub
NLTK Documentation
NLTK GitHub
NumPy Documentation
NumPy GitHub
Pandas Documentation
Pandas GitHub
Plotly Documentation
Plotly GitHub
PyPDF Documentation
PyPDF GitHub
Scikit-learn Documentation
Scikit-learn GitHub
Seaborn Documentation
Seaborn GitHub
spaCy Documentation
spaCy GitHub
TextBlob Documentation
TextBlob GitHub
WordCloud Documentation
WordCloud GitHub
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