<jupyter_start><jupyter_text>That's a lot of columns<jupyter_code>block
puma_2010_path = Path("../data/raw/ipums_puma_2010/ipums_puma_2010.shp")

us_puma = gpd.read_file(
    puma_2010_path,
    encoding="utf-8",
)

us_puma.head()

md_puma = us_puma.drop(us_puma[us_puma['STATEFIP'] != '24'].index)

md_puma.head()
md_puma.plot()
len(md_puma)
block
# Ensure that the coordinate reference system is the same
block = block.to_crs(md_puma.crs)
block.plot()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14078674948240166, 'fraction_numerical': 0.039337474120082816, 'mean_word_length': 6.223880597014926, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '309889', 'license': 'MIT', 'n_tokens_mistral': 228, 'n_tokens_neox': 214, 'n_words': 31, 'path': 'notebooks/puma_mapping.ipynb', 'repo_name': 'fedderw/maryland-child-allowance', 'size': '483'}