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 and summarizes it at the weekly level for all of 2016.

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

Explore Structure




Index Type Example Value
0 dict { }
... ... ...
Key Type Example Value Comment
"Speed" float 4.33 The average windspeed for that week, in Miles per Hour.
"Direction" int 33 The average wind direction for that week, in degrees.
Key Type Example Value Comment
"State" str "Alabama"
[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 "BHM"
[Preview ]
The unique code representing this station.
"City" str "Birmingham"
[Preview ]
The city that the reporting station sends its data to. Note that the recording station itself might actually be in a different city.
"Location" str "Birmingham, AL"
[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
"Alaska" 1719
"Texas" 1272
"California" 999
"Florida" 636
"Montana" 583
"Michigan" 477
"Nebraska" 424
"Oregon" 424
"New York" 424
"Tennessee" 420
"Washington" 371
"Missouri" 371
"Mississippi" 371
"Pennsylvania" 371
"North Carolina" 371
"Ohio" 371
"Kansas" 318
"Georgia" 318
"Nevada" 318
"Virginia" 318
"Colorado" 318
"Illinois" 318
"North Dakota" 265
"Kentucky" 265
"Iowa" 265
"South Dakota" 265
"Minnesota" 265
"Arizona" 265
"Wyoming" 265
"Hawaii" 265
"Louisiana" 261
"Indiana" 212
"South Carolina" 212
"Wisconsin" 212
"Oklahoma" 212
"West Virginia" 212
"Alabama" 212
"Arkansas" 159
"Idaho" 159
"New Mexico" 159
"Maine" 159
"Massachusetts" 153
"Connecticut" 106
"Maryland" 106
"New Jersey" 106
"New Hampshire" 100
"Puerto Rico" 53
"DE" 53
"Utah" 53
"VA" 53
"Vermont" 53
"Delaware" 53
"Rhode Island" 53
Value Count
"BHM" 53
"PDX" 53
"BCB" 53
"DCA" 53
"PVD" 53
"FSD" 53
"LAS" 53
"PHL" 53
"ILM" 53
"LZK" 53
"AKN" 53
"MIA" 53
"LIH" 53
"EKA" 53
"AST" 53
"EYW" 53
"SHR" 53
"CID" 53
"GRB" 53
"MKE" 53
"PUB" 53
"BGM" 53
"EKN" 53
"IPT" 53
"ADQ" 53
"HTS" 53
"OKC" 53
"PIR" 53
"BUF" 53
"TOL" 53
"PDT" 53
"VIH" 53
"BRW" 53
"MEM" 53
"MCN" 53
"COU" 53
"SAN" 53
"AND" 53
"CAE" 53
"IND" 53
"DDC" 53
"OME" 53
"LWS" 53
"BDL" 53
"MDT" 53
"BTR" 53
"LNK" 53
"DBQ" 53
"ROW" 53
"WAL" 53
"GLD" 53
"HLN" 53
"ATL" 53
"CSV" 53
"TUL" 53
"INL" 53
"BFF" 53
"FTW" 53
"MCI" 53
"ABR" 53
"ILG" 53
"SEA" 53
"SGY" 53
"BIS" 53
"RDU" 53
"RDM" 53
"BKW" 53
"BPT" 53
"OLM" 53
"ANJ" 53
"ERI" 53
"HVR" 53
"DRT" 53
"HKY" 53
"WMC" 53
"JAN" 53
"LEX" 53
"OTZ" 53
"LSE" 53
"TUS" 53
"ORD" 53
"HRO" 53
"CVG" 53
"FWA" 53
"LBF" 53
"MQT" 53
"AHN" 53
"UIN" 53
"CGI" 53
"MLI" 53
"TLH" 53
"SAC" 53
"CNK" 53
"DEN" 53
"EVV" 53
"GLS" 53
"DFW" 53
"RIC" 53
"SNY" 53
"DLN" 53
... ...
Value Count
"Springfield" 106
"Jackson" 106
"Columbia" 106
"Concord" 106
"Newark" 106
"Rochester" 106
"Portland" 106
"Charleston" 106
"Norfolk" 106
"Eureka" 106
"Wilmington" 106
"Tupelo" 53
"Del Rio" 53
"Evansville" 53
"Annette" 53
"Meridian" 53
"Waco" 53
"Glens Falls" 53
"Hickory" 53
"Hilo" 53
"Iliamna" 53
"Williston" 53
"Sioux City" 53
"Cleveland" 53
"Philadelphia" 53
"Washington" 53
"Tanana" 53
"Tulsa" 53
"El Paso" 53
"Sioux Falls" 53
"Cheyenne" 53
"Winnemucca" 53
"Concordia" 53
"Dallas" 53
"Mc Grath" 53
"Duluth" 53
"Sitka" 53
"Louisville" 53
"Goodland" 53
"Sandberg" 53
"Grand Island" 53
"Charlotte" 53
"Stockton" 53
"Abilene" 53
"Nashville" 53
"Huron" 53
"Orlando" 53
"Topeka" 53
"Rawlins" 53
"Austin/City" 53
"Roswell" 53
"San Antonio" 53
"Wallops Island" 53
"Jacksonville" 53
"Mercury" 53
"Casper" 53
"Fargo" 53
"Cape Hatteras" 53
"Salem" 53
"Hayward" 53
"Blacksburg" 53
"Binghamton" 53
"Fresno" 53
"Billings" 53
"Glasgow" 53
"Grand Forks" 53
"Islip" 53
"Tallahassee" 53
"Bakersfield" 53
"Ephrata" 53
"Buffalo" 53
"Clayton" 53
"Medford" 53
"Boise" 53
"Moline" 53
"Raleigh/Durham" 53
"Lincoln" 53
"Tampa" 53
"Northway" 53
"Sidney" 53
"Dallas-Fort Worth" 53
"Lynchburg" 53
"Greenville" 53
"Astoria" 53
"Anderson" 53
"Williamsport" 53
"Houghton Lake" 53
"Greenwood" 53
"Rockford" 53
"Kaunakakai" 53
"Lihue" 53
"Minneapolis" 53
"Cape Girardeau" 53
"Tucson" 53
"Grand Rapids" 53
"Greer" 53
"Bangor" 53
"Kingman" 53
"Hattiesburg" 53
"Redmond" 53
... ...
Value Count
"Richmond, VA" 53
"Anderson, SC" 53
"Charlotte, NC" 53
"Austin/Bergstrom, TX" 53
"Houston, TX" 53
"Cordova, AK" 53
"Butte, MT" 53
"Savannah, GA" 53
"Windsor Locks, CT" 53
"Salem, OR" 53
"Chattanooga, TN" 53
"Annette, AK" 53
"Allentown, PA" 53
"Cut Bank, MT" 53
"Williston, ND" 53
"Marquette, MI" 53
"Rolla/Vichy, MO" 53
"Cape Hatteras, NC" 53
"Tallahassee, FL" 53
"Miles City, MT" 53
"Redmond, OR" 53
"San Antonio, TX" 53
"Olympia, WA" 53
"Washington, VA" 53
"Topeka, KS" 53
"Oklahoma City, OK" 53
"North Platte, NE" 53
"Columbus, OH" 53
"King Salmon, AK" 53
"Covington, KY" 53
"Bethel, AK" 53
"Duluth, MN" 53
"Gainesville, FL" 53
"Brownsville, TX" 53
"Bristol/Jhnsn Cty/Kngsprt, TN" 53
"Columbia, MO" 53
"Del Rio, TX" 53
"Mercury, NV" 53
"Abilene, TX" 53
"Tanana, AK" 53
"New York, NY" 53
"Hilo, HI" 53
"Boise, ID" 53
"Omaha, NE" 53
"Sidney, NE" 53
"San Diego, CA" 53
"Mobile, AL" 53
"Huntsville, AL" 53
"Nome, AK" 53
"Youngstown/Warren, OH" 53
"Norfolk, VA" 53
"Houghton Lake, MI" 53
"Ephrata, WA" 53
"Denver, CO" 53
"Grand Rapids, MI" 53
"Rockford, IL" 53
"Portland, ME" 53
"Aberdeen, SD" 53
"Casper, WY" 53
"Honolulu, HI" 53
"Fresno, CA" 53
"Kahului, HI" 53
"Lewiston, ID" 53
"Crossville, TN" 53
"Havre, MT" 53
"Grand Island, NE" 53
"Alma, GA" 53
"Columbia, SC" 53
"Bridgeport, CT" 53
"Yakutat, AK" 53
"Helena, MT" 53
"Daytona Beach, FL" 53
"Phoenix, AZ" 53
"Louisville, KY" 53
"Tupelo, MS" 53
"Bakersfield, CA" 53
"Medford, OR" 53
"Salisbury, MD" 53
"Akron, OH" 53
"San Francisco, CA" 53
"Corpus Christi, TX" 53
"Gulfport, MS" 53
"Madison, WI" 53
"Pittsburgh, PA" 53
"Fort Worth, TX" 53
"Rapid City, SD" 53
"New Orleans, LA" 53
"Dallas, TX" 53
"Sheridan, WY" 53
"La Crosse, WI" 53
"Vero Beach, FL" 53
"Eureka, NV" 53
"Dayton, OH" 53
"Sioux Falls, SD" 53
"Erie, PA" 53
"Sitka, AK" 53
"Long Beach, CA" 53
"Yakima, WA" 53
"Wilmington, NC" 53
"Santa Barbara, CA" 53
... ...
Key Type Example Value Comment
"Data" dict { }
"Date" dict { }
"Station" dict { }
Key Type Example Value Comment
"Week of" int 3 The first day of the week that this report was made.
"Month" int 1 The month of the year that this report was made.
"Full" str "2016-01-03"
[Preview ]
A full string representation of the date this report was made.
"Year" int 2016 The year that this report was made.
Value Count
"2016-05-29" 317
"2016-10-02" 317
"2016-08-07" 317
"2016-10-16" 317
"2016-04-03" 317
"2016-10-23" 317
"2016-08-14" 317
"2016-10-30" 317
"2016-06-05" 317
"2016-05-01" 317
"2016-08-28" 317
"2017-01-01" 317
"2016-12-04" 317
"2016-09-04" 317
"2016-08-21" 317
"2016-03-06" 317
"2016-05-22" 317
"2016-12-18" 317
"2016-12-25" 317
"2016-05-08" 317
"2016-11-06" 317
"2016-12-11" 317
"2016-05-15" 317
"2016-10-09" 317
"2016-04-24" 316
"2016-09-18" 316
"2016-09-25" 316
"2016-04-17" 316
"2016-07-03" 316
"2016-09-11" 316
"2016-04-10" 316
"2016-01-24" 315
"2016-02-14" 315
"2016-06-26" 315
"2016-06-19" 315
"2016-02-07" 315
"2016-01-10" 315
"2016-03-20" 315
"2016-01-03" 315
"2016-03-27" 315
"2016-03-13" 315
"2016-06-12" 315
"2016-01-31" 315
"2016-02-28" 315
"2016-02-21" 315
"2016-01-17" 315
"2016-11-13" 314
"2016-07-31" 314
"2016-07-17" 314
"2016-11-20" 314
"2016-07-24" 314
"2016-07-10" 314
"2016-11-27" 314
Key Type Example Value Comment
"Avg Temp" int 39 The average recorded temperature on this week, in degrees Farenheit.
"Max Temp" int 46 The highest recorded temperature on this week, in degrees Farenheit.
"Min Temp" int 32 The lowest recorded temperature on this week, in degrees Farenheit.
Key Type Example Value Comment
"Temperature" dict { }
"Wind" dict { }
"Precipitation" float 0.0 The average amount of rain, in inches.

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.