Rename category and add several libraries

[morags]

This PR has several parts:

1. Rename "Geolocation" to "geospatial data processing", since it's a more accurate description of what these libraries do.
2. Create a new subcategory for the "core" libraries and frameworks that are used for data analysis in the Python ecosystem.
3. Move or add many popular libraries for visuzaliation and [exploratory] data analysis (see below).

Added / moves libraries:

1. Cartopy, which is a complete geospatial data processing suit.
2. GeoPandas, a geospatial data processing suit that is part of the HoloViz suit.
3. TextBlob, a high-level API to NLTK.
4. Dataprep, Pandas Profiling, SweetViz and Lux, which are "exploratory data analysis" tools that can automatically visualize a dataset.
5. Folium, an API for rendering spatial data using Leaflet.js
6. Glue, which is used to visualize data across different domains.
7. HoloViz, a suit of modern visualization libraries.
8. napari, is an advanced image viewer and annotator that can deal with very large images and layered data.
9. D-Tale, a spreadsheet-like interface to Pandas DFs.
10. xarray, a Pandas alternative for >3-dimensional data.
11. PyArrow, an interface to Apache Arrow.
12. bamboolib, a gui for visualizing and tranforming DFs.
13. Turi Create, a machine learning framework from Apple.
14. Polars, a fast Pandas alternative written in Rust.
15. Vaex, an Arrow-NumPy hybrid for large datasets.
16. RAPIDS, a suit of libraries for running data analysis tasks on GPUs.
17. Modin, a Pandas-like API on top of Dask and Ray.