🌿 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