<jupyter_start><jupyter_text>Define data<jupyter_code>tiles = gv.tile_sources.Wikipedia 

# Project data to Web Mercator
nybb = gpd.read_file(gpd.datasets.get_path('nybb'))
poly_data = nybb.to_crs(ccrs.GOOGLE_MERCATOR.proj4_init)
polys = gv.Polygons(poly_data, vdims='BoroName', crs=ccrs.GOOGLE_MERCATOR)<jupyter_output><empty_output><jupyter_text>Plot<jupyter_code>tiles.opts(zoom=10) * polys.opts(color='BoroName', cmap='tab20', fig_size=200, padding=0.1)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14168377823408623, 'fraction_numerical': 0.02053388090349076, 'mean_word_length': 10.902439024390244, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '279009', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 226, 'n_tokens_neox': 210, 'n_words': 20, 'path': 'examples/gallery/matplotlib/new_york_boroughs.ipynb', 'repo_name': 'vishalbelsare/geoviews', 'size': '487'}