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# United States of America Python Dictionary to translate States, | |
# Districts & Territories to Two-Letter codes and vice versa. | |
# | |
# Canonical URL: https://gist.github.com/rogerallen/1583593 | |
# | |
# Dedicated to the public domain. To the extent possible under law, | |
# Roger Allen has waived all copyright and related or neighboring | |
# rights to this code. Data originally from Wikipedia at the url: | |
# https://en.wikipedia.org/wiki/ISO_3166-2:US | |
# | |
# Automatically Generated 2021-09-11 18:04:36 via Jupyter Notebook from | |
# https://gist.github.com/rogerallen/d75440e8e5ea4762374dfd5c1ddf84e0 | |
us_state_to_abbrev = { | |
"Alabama": "AL", | |
"Alaska": "AK", | |
"Arizona": "AZ", | |
"Arkansas": "AR", | |
"California": "CA", | |
"Colorado": "CO", | |
"Connecticut": "CT", | |
"Delaware": "DE", | |
"Florida": "FL", | |
"Georgia": "GA", | |
"Hawaii": "HI", | |
"Idaho": "ID", | |
"Illinois": "IL", | |
"Indiana": "IN", | |
"Iowa": "IA", | |
"Kansas": "KS", | |
"Kentucky": "KY", | |
"Louisiana": "LA", | |
"Maine": "ME", | |
"Maryland": "MD", | |
"Massachusetts": "MA", | |
"Michigan": "MI", | |
"Minnesota": "MN", | |
"Mississippi": "MS", | |
"Missouri": "MO", | |
"Montana": "MT", | |
"Nebraska": "NE", | |
"Nevada": "NV", | |
"New Hampshire": "NH", | |
"New Jersey": "NJ", | |
"New Mexico": "NM", | |
"New York": "NY", | |
"North Carolina": "NC", | |
"North Dakota": "ND", | |
"Ohio": "OH", | |
"Oklahoma": "OK", | |
"Oregon": "OR", | |
"Pennsylvania": "PA", | |
"Rhode Island": "RI", | |
"South Carolina": "SC", | |
"South Dakota": "SD", | |
"Tennessee": "TN", | |
"Texas": "TX", | |
"Utah": "UT", | |
"Vermont": "VT", | |
"Virginia": "VA", | |
"Washington": "WA", | |
"West Virginia": "WV", | |
"Wisconsin": "WI", | |
"Wyoming": "WY", | |
"District of Columbia": "DC", | |
"American Samoa": "AS", | |
"Guam": "GU", | |
"Northern Mariana Islands": "MP", | |
"Puerto Rico": "PR", | |
"United States Minor Outlying Islands": "UM", | |
"U.S. Virgin Islands": "VI", | |
} | |
# invert the dictionary | |
abbrev_to_us_state = dict(map(reversed, us_state_to_abbrev.items())) |
Thank you!
Thank you!! And Happy New Year!
I just came in today to parse legal data from all US states and this came very in handy! Thanks buddy!
us_state_to_abbrev = {
"Alabama": "AL",
"Alaska": "AK",
"Arizona": "AZ",
"Arkansas": "AR",
"California": "CA",
"Colorado": "CO",
"Connecticut": "CT",
"Delaware": "DE",
"Florida": "FL",
"Georgia": "GA",
"Hawaii": "HI",
"Idaho": "ID",
"Illinois": "IL",
"Indiana": "IN",
"Iowa": "IA",
"Kansas": "KS",
"Kentucky": "KY",
"Louisiana": "LA",
"Maine": "ME",
"Maryland": "MD",
"Massachusetts": "MA",
"Michigan": "MI",
"Minnesota": "MN",
"Mississippi": "MS",
"Missouri": "MO",
"Montana": "MT",
"Nebraska": "NE",
"Nevada": "NV",
"New Hampshire": "NH",
"New Jersey": "NJ",
"New Mexico": "NM",
"New York": "NY",
"North Carolina": "NC",
"North Dakota": "ND",
"Ohio": "OH",
"Oklahoma": "OK",
"Oregon": "OR",
"Pennsylvania": "PA",
"Rhode Island": "RI",
"South Carolina": "SC",
"South Dakota": "SD",
"Tennessee": "TN",
"Texas": "TX",
"Utah": "UT",
"Vermont": "VT",
"Virginia": "VA",
"Washington": "WA",
"West Virginia": "WV",
"Wisconsin": "WI",
"Wyoming": "WY",
"District of Columbia": "DC",
"American Samoa": "AS",
"Guam": "GU",
"Northern Mariana Islands": "MP",
"Puerto Rico": "PR",
"United States Minor Outlying Islands": "UM",
"U.S. Virgin Islands": "VI",
"AL": "AL",
"AK": "AK",
"AZ": "AZ",
"AR": "AR",
"CA": "CA",
"CO": "CO",
"CT": "CT",
"DE": "DE",
"FL": "FL",
"GA": "GA",
"HI": "HI",
"ID": "ID",
"IL": "IL",
"IN": "IN",
"IA": "IA",
"KS": "KS",
"KY": "KY",
"LA": "LA",
"ME": "ME",
"MD": "MD",
"MA": "MA",
"MI": "MI",
"MN": "MN",
"MS": "MS",
"MO": "MO",
"MT": "MT",
"NE": "NE",
"NV": "NV",
"NH": "NH",
"NJ": "NJ",
"NM": "NM",
"NY": "NY",
"NC": "NC",
"ND": "ND",
"OH": "OH",
"OK": "OK",
"OR": "OR",
"PA": "PA",
"RI": "RI",
"SC": "SC",
"SD": "SD",
"TN": "TN",
"TX": "TX",
"UT": "UT",
"VT": "VT",
"VA": "VA",
"WA": "WA",
"WV": "WV",
"WI": "WI",
"WY": "WY",
"DC": "DC",
"AS": "AS",
"GU": "GU",
"MP": "MP",
"PR": "PR",
"UM": "UM",
"VI": "VI",
}
needed this for standardizing the column where some values were full names and some were abbreviations
@pnojai take a look at : https://pypi.org/project/us/
thanks a lot
Thank you for sharing this information
@sandlerj It's worked very well, and thank you
Thank you!
Commenting as well, so everyone gets spam on this gist! haha
Here is one to encode US states based on their population ranking (might be usefull for some further correlation analysis):
us_state_to_population_ranking_encoding = {
"CA": 52, "TX": 51, "FL": 50, "NY": 49, "PA": 48, "IL": 47,
"OH": 46, "GA": 45, "NC": 44, "MI": 43, "NJ": 42, "VA": 41,
"WA": 40, "AZ": 39, "TN": 38, "MA": 37, "IN": 36, "MO": 35,
"MD": 34, "CO": 33, "WI": 32, "MN": 31, "SC": 30, "AL": 29,
"LA": 28, "KY": 27, "OR": 26, "OK": 25, "CT": 24, "UT": 23,
"PR": 22, "NV": 21, "IA": 20, "AR": 19, "MS": 18, "KS": 17,
"NM": 16, "NE": 15, "ID": 14, "WV": 13, "HI": 12, "NH": 11,
"ME": 10, "MT": 9, "RI": 8, "DE": 7, "SD": 6, "ND": 5,
"AK": 4, "DC": 3, "VT": 2, "WY": 1
}