<jupyter_start><jupyter_text>Language Usage for states, counties, and census tribal areas- [For states](state_overview) see detailed data on [WIC utilization](utilization), [State-level, detailed language data](state-lang), and [County-level, broad language data](county-lang)- [For census tribal areas](tibal-overview), [tribe-level, broad language data](tribal)All language and populatioen data are from the [2019 American Community Survey 5-year estimates](https://www.census.gov/programs-surveys/acs). WIC Data are from [National- and State-Level Estimates of WIC Eligibility and WIC Program Reach in 2018 With Updated Estimates for 2016 and 2017](https://www.fns.usda.gov/wic/national-and-state-level-estimates-wic-eligibility-and-wic-program-reach-2018-updated).  Who speaks which languages in which countiesBelow are the following data about states:- WIC Participation rates for the state, per WIC's 2018 Coverage Rates- Detailed language breakdowns of the percentage of speakers of languages in the state who do not speak English well- County-level breakdowns of who percentages of speakers of languages for speakers who do not speak English wellCounties are sorted by population (highest population first).<jupyter_code>interact(get_styled_census_data, state=get_state_fips_codes().items())<jupyter_output><empty_output><jupyter_text>Who speaks which languages in tribesBecause tribal areas tend to be smaller, few have the detailed breakdowns of the state-data above. However, we can still get broad counts like counties, as below.<jupyter_code>interact(get_styled_tribal_data, tribe=get_tribal_area_names().items())<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08031400966183574, 'fraction_numerical': 0.015096618357487922, 'mean_word_length': 6.497737556561086, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 2, 'lorem ipsum': 0, 'www.': 2, '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': '607802', 'license': 'CC0-1.0', 'n_tokens_mistral': 495, 'n_tokens_neox': 444, 'n_words': 171, 'path': 'census_data_analysis.ipynb', 'repo_name': 'mattbowen-usds/benefits-census-analysis', 'size': '1656'}