๐ฟ Environmental Science
Basemap
Description: A toolkit for plotting 2D data on maps in Python.
Use Case: Creating geographical maps, useful for environmental data visualization like climate patterns and land use changes.
Documentation: Basemap Documentation
GitHub Repository: Basemap GitHub
EarthPy
Description: A collection of Python tools for working with spatial and environmental data.
Use Case: Facilitating the use of spatial data for environmental science, especially for earth and environmental science disciplines.
Documentation: EarthPy Documentation
GitHub Repository: EarthPy GitHub
Fiona
Description: A tool for reading and writing spatial data files.
Use Case: Handling geographic data, crucial in environmental sciences for spatial analysis.
Documentation: Fiona Documentation
GitHub Repository: Fiona GitHub
Geopandas
Description: Extends Pandas for spatial data operations.
Use Case: Integrating spatial data with traditional data types for geographic data analysis, like environmental monitoring and land use studies.
Documentation: GeoPandas Documentation
GitHub Repository: GeoPandas GitHub
Matplotlib
Description: A library for creating static, animated, and interactive visualizations in Python.
Use Case: Generating plots and graphs for environmental data visualization, such as temperature trends, pollution levels, and biodiversity studies.
Documentation: Matplotlib Documentation
GitHub Repository: Matplotlib GitHub
NumPy
Description: Fundamental package for scientific computing with Python.
Use Case: Handling numerical data, performing calculations, and statistical analysis in environmental science research.
Documentation: NumPy Documentation
GitHub Repository: NumPy GitHub
Pandas
Description: Data analysis and manipulation library.
Use Case: Organizing, analyzing, and manipulating environmental datasets, such as climate data or species distribution records.
Documentation: Pandas Documentation
GitHub Repository: Pandas GitHub
Plotly
Description: An interactive graphing library.
Use Case: Creating interactive and dynamic visualizations of environmental data, useful in presenting complex environmental phenomena.
Documentation: Plotly Documentation
GitHub Repository: Plotly GitHub
PyProj
Description: A Python interface to PROJ (cartographic projections and coordinate transformations library).
Use Case: Handling geospatial coordinate transformations and projections in environmental studies.
Documentation: PyProj Documentation
GitHub Repository: PyProj GitHub
Rasterio
Description: A library for raster data processing.
Use Case: Working with satellite imagery and geospatial raster data, such as land cover analysis and remote sensing.
Documentation: Rasterio Documentation
GitHub Repository: Rasterio GitHub
Scikit-learn
Description: Machine learning in Python.
Use Case: Predictive modeling and statistical analysis in environmental science, such as habitat modeling and climate change predictions.
Documentation: Scikit-learn Documentation
GitHub Repository: Scikit-learn GitHub
SciPy
Description: An open-source Python library used for scientific and technical computing.
Use Case: Scientific computations and simulations in environmental science, including data analysis and modeling of environmental systems.
Documentation: SciPy Documentation
GitHub Repository: SciPy GitHub
Seaborn
Description: A Python data visualization library based on Matplotlib.
Use Case: Creating informative and attractive statistical graphics in environmental science.
Documentation: Seaborn Documentation
GitHub Repository: Seaborn GitHub
Xarray
Description: An open-source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!
Use Case: Handling multi-dimensional datasets, commonly used in environmental sciences, such as meteorological and oceanographic data.
Documentation: Xarray Documentation
GitHub Repository: Xarray GitHub
Last updated