Created by: morags
This PR has several parts:
- Rename "Geolocation" to "geospatial data processing", since it's a more accurate description of what these libraries do.
- Create a new subcategory for the "core" libraries and frameworks that are used for data analysis in the Python ecosystem.
- Move or add many popular libraries for visuzaliation and [exploratory] data analysis (see below).
Added / moves libraries:
- Cartopy, which is a complete geospatial data processing suit.
- GeoPandas, a geospatial data processing suit that is part of the HoloViz suit.
- TextBlob, a high-level API to NLTK.
- Dataprep, Pandas Profiling, SweetViz and Lux, which are "exploratory data analysis" tools that can automatically visualize a dataset.
- Folium, an API for rendering spatial data using Leaflet.js
- Glue, which is used to visualize data across different domains.
- HoloViz, a suit of modern visualization libraries.
- napari, is an advanced image viewer and annotator that can deal with very large images and layered data.
- D-Tale, a spreadsheet-like interface to Pandas DFs.
- xarray, a Pandas alternative for >3-dimensional data.
- PyArrow, an interface to Apache Arrow.
- bamboolib, a gui for visualizing and tranforming DFs.
- Turi Create, a machine learning framework from Apple.
- Polars, a fast Pandas alternative written in Rust.
- Vaex, an Arrow-NumPy hybrid for large datasets.
- RAPIDS, a suit of libraries for running data analysis tasks on GPUs.
- Modin, a Pandas-like API on top of Dask and Ray.