<jupyter_start><jupyter_text>Search metadata<jupyter_code>catalog = Catalog()
countries = catalog.countries
countries
usa = countries.get('usa')
usa
datasets = usa.datasets
datasets
dataset = datasets.get('od_acs_d28e63ff')
variables = dataset.variables
variables
variables_filtered = [variables.get('median_income_646ff1a1')]
variables_filtered<jupyter_output><empty_output><jupyter_text>Enrichment<jupyter_code>credentials = Credentials.from_file()
set_default_credentials(credentials)

data = pd.read_csv('./data/points.csv')
data.head()
# You may need access to Data Observatory for this
data_augmentated = Enrichment().enrich_points(data, variables_filtered, data_geom_column='geom')
# Note the enrichment process also may include `area_XXX` columns from the original data geometries
data_augmentated.head()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.0997624703087886, 'fraction_numerical': 0.010688836104513063, 'mean_word_length': 9.407407407407407, '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': '125416', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 287, 'n_tokens_neox': 269, 'n_words': 53, 'path': 'examples/_debug/do_points_enrichment.ipynb', 'repo_name': 'jorisvandenbossche/cartoframes', 'size': '842'}