Overview

Under the National Oceanic and Atmpospheric Administration, the National Weather Service provides daily weather reports for cities across the county. This is done through the use of 122 different Weather Forcast Offices throughout the country. These WFOs are responsible for the daily weather reports for serveral cities throughout their region of coverage. This data set takes the information from these WFO reports for cities across the country through the months of April to June of 2016.

http://w2.weather.gov/climate/

Explore Structure




Index Type Example Value
0 dict { }
... ... ...
Index Type Example Value
0 str "RAIN"
... ... ...
Key Type Example Value Comment
"Full" str "04-10-2016"
[Preview ]
A full string representation of the date this report was made.
"Day" int 10 The day of the month that this report was made.
"Month" int 4 The month of the year that this report was made.
"Year" int 2016 The year that this report was made.
Value Count
"05-23-2016" 130
"05-04-2016" 130
"04-11-2016" 130
"04-29-2016" 130
"05-09-2016" 130
"05-15-2016" 130
"04-17-2016" 130
"04-30-2016" 130
"04-27-2016" 130
"05-10-2016" 130
"04-15-2016" 130
"05-18-2016" 130
"04-28-2016" 130
"04-16-2016" 130
"06-06-2016" 130
"05-31-2016" 130
"05-22-2016" 130
"05-29-2016" 130
"06-04-2016" 130
"05-24-2016" 130
"05-21-2016" 130
"06-07-2016" 130
"05-30-2016" 130
"06-02-2016" 130
"05-17-2016" 130
"05-25-2016" 130
"05-12-2016" 130
"05-08-2016" 130
"05-03-2016" 130
"05-11-2016" 130
"04-14-2016" 130
"05-01-2016" 130
"05-13-2016" 130
"05-19-2016" 130
"05-26-2016" 130
"05-28-2016" 130
"05-02-2016" 130
"04-22-2016" 130
"04-13-2016" 130
"04-12-2016" 130
"05-07-2016" 130
"06-05-2016" 130
"04-20-2016" 130
"04-25-2016" 130
"05-20-2016" 130
"05-27-2016" 130
"04-23-2016" 130
"04-18-2016" 130
"06-03-2016" 130
"04-26-2016" 130
"06-01-2016" 130
"06-09-2016" 130
"04-19-2016" 130
"05-05-2016" 130
"05-14-2016" 130
"06-08-2016" 130
"05-06-2016" 130
"04-21-2016" 130
"05-16-2016" 130
"04-24-2016" 128
"04-10-2016" 105
Key Type Example Value Comment
"Precipitation" float 0.19 The average amount of rain, in inches.
"Temperature" dict { }
"Wind" dict { }
Key Type Example Value Comment
"High Gust" float 31.0 A gust is a sudden burst of wind (less than 20 seconds long), which is often much stronger than the average windspeed. This is reported in Miles per Hour.
"Avg Wind" float 8.0 The average windspeed for that day, in Miles per Hour.
"High Wind" float 22.0 The highest windspeed for that day, in Miles per Hour.
Key Type Example Value Comment
"Weather Conditions" list [ ] This is a list of strings. An empty list represents no significant weather to report for that day.
"Date" dict { }
"Station" dict { }
"Data" dict { }
Key Type Example Value Comment
"Max Temp" float 40.0 The highest recorded temperature on this day, in degrees Farenheit.
"Avg Temp" float 34.0 The average recorded temperature on this day, in degrees Farenheit.
"Min Temp" float 27.0 The lowest recorded temperature on this day, in degrees Farenheit.
Key Type Example Value Comment
"City" str "Mount Holly/Philadelphia"
[Preview ]
The city that the reporting station sends its data to. Note that the recording station itself might actually be in a different city.
"State" str "New Jersey"
[Preview ]
The state that the reporting station sends its data to. Note that the recording station itself might actually be in a different state.
"Code" str "ABE"
[Preview ]
The unique code representing this station.
"Location" str "Mount Holly/Philadelphia, NJ"
[Preview ]
The exact location of this recording station. Note that the recording station itself might be in a different location than where it sends its data.
Value Count
"Sioux Falls" 183
"Minneapolis/Twin Cities" 122
"Marquette" 122
"Charleston" 122
"La Crosse" 122
"Grand Junction" 122
"Green Bay" 122
"Milwaukee/Sullivan" 122
"Wilmington" 122
"Grand Forks" 122
"Rapid City" 122
"Jackson" 122
"Sacramento" 120
"Des Moines" 61
"Davenport/Quad Cities" 61
"Morristown/Knoxville" 61
"St. Louis" 61
"Gray/Portland" 61
"Greenville-Spartanburg" 61
"Raleigh" 61
"Melbourne" 61
"Peachtree City/Atlanta" 61
"Pueblo" 61
"Jacksonville" 61
"Grand Rapids" 61
"Pocatello" 61
"Amarillo" 61
"Nashville" 61
"Huntsville" 61
"Phoenix" 61
"Norman/Oklahoma City" 61
"Springfield" 61
"Lubbock" 61
"Bismarck" 61
"Chicago" 61
"Houston/Galveston" 61
"Northern Indiana" 61
"Cleveland" 61
"Birmingham" 61
"New Orleans/Baton Rouge" 61
"North Platte" 61
"Goodland" 61
"Midland/Odessa" 61
"Shreveport" 61
"Lincoln" 61
"Lake Charles" 61
"Kansas City/Pleasant Hill" 61
"Memphis" 61
"Billings" 61
"Great Falls" 61
"Binghamton" 61
"Buffalo" 61
"Indianapolis" 61
"Louisville" 61
"Aberdeen" 61
"Detroit/Pontiac" 61
"New York/Upton" 61
"Boston" 61
"Brownsville" 61
"Caribou" 61
"Blacksburg" 61
"Mount Holly/Philadelphia" 61
"Newport/Morehead City" 61
"Riverton" 61
"Omaha/Valley" 61
"San Diego" 61
"Pittsburgh" 61
"Key West" 61
"Cheyenne" 61
"Burlington" 61
"Tallahassee" 61
"San Juan" 61
"Mobile/Pensacola" 61
"Salt Lake City" 61
"North Little Rock" 61
"Corpus Christi" 61
"Austin/San Antonio" 61
"Dodge City" 61
"Topeka" 61
"Wakefield" 61
"Miami" 61
"Paducah" 61
"Tampa" 61
"Baltimore/Washington" 61
"El Paso" 61
"Tulsa" 61
"Columbia" 61
"Gaylord" 61
"Fort Worth/Dallas" 61
"State College" 61
"Albany" 61
"Portland" 60
"Juneau" 60
"Reno" 60
"Elko" 60
"San Francisco Bay Area/Monterey" 60
"Wichita" 60
"Anchorage" 60
"San Angelo" 60
"Glasgow" 60
... ...
Value Count
"Texas" 609
"California" 421
"South Dakota" 366
"Wisconsin" 366
"Florida" 366
"Michigan" 305
"New York" 244
"Colorado" 243
"Kansas" 243
"Montana" 242
"North Carolina" 183
"Alabama" 183
"Missouri" 183
"Kentucky" 183
"North Dakota" 183
"South Carolina" 183
"Tennessee" 183
"Virginia" 183
"Louisiana" 183
"Nebraska" 182
"Arizona" 181
"Nevada" 180
"Alaska" 180
"Oregon" 180
"Maine" 122
"Pennsylvania" 122
"Iowa" 122
"Indiana" 122
"Ohio" 122
"Illinois" 122
"Minnesota" 122
"Wyoming" 122
"Oklahoma" 122
"Idaho" 121
"Washington" 120
"New Jersey" 61
"Puerto Rico" 61
"Georgia" 61
"Vermont" 61
"Massachusetts" 61
"West Virginia" 61
"Utah" 61
"Arkansas" 61
"Mississippi" 61
"New Mexico" 60
Value Count
"FSD" 183
"RST" 122
"ORD" 122
"UNR" 122
"INL" 122
"ANJ" 122
"DEN" 122
"FGF" 122
"MQT" 122
"RDD" 120
"PWM" 61
"SDF" 61
"MCI" 61
"ARR" 61
"CNK" 61
"HOU" 61
"ABR" 61
"STL" 61
"ABE" 61
"SGF" 61
"BIS" 61
"LZK" 61
"DAB" 61
"BIL" 61
"CYS" 61
"CAE" 61
"CVG" 61
"LOZ" 61
"TOL" 61
"LOT" 61
"GNV" 61
"ALS" 61
"TUL" 61
"BRO" 61
"PUB" 61
"ALB" 61
"BTR" 61
"MEM" 61
"OMA" 61
"TLH" 61
"ELP" 61
"BBW" 61
"CUB" 61
"AUS" 61
"SJU" 61
"DFW" 61
"CZZ" 61
"RDU" 61
"MDT" 61
"JKL" 61
"CGI" 61
"BZN" 61
"ILM" 61
"ATL" 61
"LCH" 61
"MOB" 61
"IND" 61
"OKC" 61
"MTH" 61
"ANB" 61
"BTV" 61
"RNK" 61
"DSM" 61
"DGC" 61
"GRR" 61
"BUF" 61
"AMA" 61
"DTW" 61
"GJT" 61
"CRP" 61
"MAF" 61
"APN" 61
"PHX" 61
"BNA" 61
"BYI" 61
"CHS" 61
"JAN" 61
"BGM" 61
"JAX" 61
"CHA" 61
"RIC" 61
"PIT" 61
"LBF" 61
"HSV" 61
"LBB" 61
"DCA" 61
"PIA" 61
"HSE" 61
"NYC" 61
"BGR" 61
"LIT" 61
"FWA" 61
"BOS" 61
"MIA" 61
"BUR" 60
"STS" 60
"ABQ" 60
"ABI" 60
"CNU" 60
"FAI" 60
... ...
Value Count
"Sioux Falls, SD" 183
"Grand Forks, ND" 122
"Green Bay, WI" 122
"La Crosse, WI" 122
"Milwaukee/Sullivan, WI" 122
"Grand Junction, CO" 122
"Minneapolis/Twin Cities, MN" 122
"Marquette, MI" 122
"Rapid City, SD" 122
"Sacramento, CA" 120
"Jacksonville, FL" 61
"Omaha/Valley, NE" 61
"New York/Upton, NY" 61
"Chicago, IL" 61
"Charleston, SC" 61
"Pueblo, CO" 61
"Norman/Oklahoma City, OK" 61
"Topeka, KS" 61
"Miami, FL" 61
"Nashville, TN" 61
"Grand Rapids, MI" 61
"Salt Lake City, UT" 61
"Binghamton, NY" 61
"Tampa, FL" 61
"St. Louis, MO" 61
"Detroit/Pontiac, MI" 61
"Albany, NY" 61
"Austin/San Antonio, TX" 61
"Lubbock, TX" 61
"Springfield, MO" 61
"Houston/Galveston, TX" 61
"Raleigh, NC" 61
"Midland/Odessa, TX" 61
"Cleveland, OH" 61
"Burlington, VT" 61
"Lake Charles, LA" 61
"Cheyenne, WY" 61
"Mount Holly/Philadelphia, NJ" 61
"North Little Rock, AR" 61
"Buffalo, NY" 61
"Davenport/Quad Cities, IA" 61
"Key West, FL" 61
"Phoenix, AZ" 61
"Newport/Morehead City, NC" 61
"Louisville, KY" 61
"Corpus Christi, TX" 61
"Gaylord, MI" 61
"Pittsburgh, PA" 61
"San Diego, CA" 61
"Columbia, SC" 61
"Fort Worth/Dallas, TX" 61
"Wilmington, NC" 61
"Peachtree City/Atlanta, GA" 61
"Northern Indiana, IN" 61
"Jackson, MS" 61
"Birmingham, AL" 61
"San Juan, PR" 61
"Lincoln, IL" 61
"Melbourne, FL" 61
"State College, PA" 61
"Memphis, TN" 61
"Billings, MT" 61
"Charleston, WV" 61
"Boston, MD" 61
"Gray/Portland, ME" 61
"Kansas City/Pleasant Hill, MO" 61
"Mobile/Pensacola, AL" 61
"Indianapolis, IN" 61
"Huntsville, AL" 61
"Goodland, KS" 61
"Blacksburg, VA" 61
"Riverton, WY" 61
"Pocatello, ID" 61
"Wilmington, OH" 61
"Shreveport, LA" 61
"Greenville-Spartanburg, SC" 61
"North Platte, NE" 61
"Dodge City, KS" 61
"New Orleans/Baton Rouge, LA" 61
"Jackson, KY" 61
"Morristown/Knoxville, TN" 61
"Wakefield, VA" 61
"Brownsville, TX" 61
"Great Falls, MT" 61
"Baltimore/Washington, VA" 61
"Tallahassee, FL" 61
"Amarillo, TX" 61
"Paducah, KY" 61
"Caribou, ME" 61
"Bismarck, ND" 61
"El Paso, TX" 61
"Aberdeen, SD" 61
"Tulsa, OK" 61
"Des Moines, IA" 61
"Elko, NV" 60
"Denver/Boulder, CO" 60
"Albuquerque, NM" 60
"Anchorage, AK" 60
"Las Vegas, NV" 60
"San Angelo, TX" 60
... ...

Downloads

Download all of the following files.

Usage

This library has 1 function you can use.
import weather
list_of_report = weather.get_weather()
Additionally, some of the functions can return a sample of the Big Data using an extra argument. If you use this sampled Big Data, it may be much faster. When you are sure your code is correct, you can remove the argument to use the full dataset.
import weather
# These may be slow!
list_of_report = weather.get_weather(test=True)

Documentation

 weather.get_weather(test=False)

Returns weather reports from the dataset.