Overview

The Advance Monthly and Monthly Retail Trade Surveys (MARTS and MRTS), the Annual Retail Trade Survey (ARTS), and the Quarterly E-Commerce Report work together to produce the most comprehensive data available on retail economic activity in the United States. More detailed descriptions of these programs can be found by choosing one of the links to the left. Regular quality control and verification takes place between MARTS, MRTS, and ARTS annually and between these programs and the Economic Census of Retail Trade every five years. Each year when annual data become available, we compare and resolve differences between the data collected on the monthly and annual surveys. We refer to this process as the monthly-to-annual reconciliation. At the same time, we benchmark the monthly estimates using results of the annual survey. ARTS estimates are then benchmarked to data maintained by the Economic Census of Retail Trade. This process of benchmarking retail data over all four programs ensures consistency in our estimates. The Business Expenses Supplement is an addition to the 2007 Annual Retail Trade Survey. Its purpose is to compile statistics on detailed business operating expenses. The United States Code, Title 13, authorizes this program as part of the Economic Census. This coverage was previously part of the predecessor Business Expenses Survey. Detailed expenses are collected every five years.

http://www.census.gov/retail/about_the_surveys.html

Explore Structure




Index Type Example Value
0 dict { }
... ... ...
Key Type Example Value Comment
"floor covering stores" int 0
"grocery stores" int 0
"limited service eating places" int 0
"shoe stores" int 0
"health and personal care stores" int 0
"miscellaneous store retailers" int 0
"sporting goods stores" int 0
"family clothing stores" int 0
"drinking places" int 0
"other general merchandise stores" int 0
"home furnishings stores" int 0
"hardware stores" int 0
"auto and other motor vehicles" int 0
"food and beverage stores" int 26963
"men's clothing stores" int 0
"sporting goods, hobby, book, and music stores" int 0
"used merchandise stores" int 0
"furniture, home furn, electronics, and appliance stores" int 13850
"gasoline stations" int 0
"other clothing stores" int 0
"office supplies, stationery, and gift stores" int 0
"discount department stores" int 0
"building supplies dealers" int 0
"automotive parts and tire stores" int 0
"general merchandise stores" int 43750
"motor vehicle and parts dealers" int 62619
"all other merchandise stores" int 0
"new car dealers" int 0
"household appliance stores" int 0
"electronics and appliance stores" int 0
"non-leased department stores" int 33798
"furniture and home furnishings stores" int 0
"all department stores" int 0
"pharmacies and drug stores" int 0
"radio, TV, and electronics stores" int 0
"food services and drinking places" int 0
"appliances and other electronics stores" int 0
"beer, wine, and liquor stores" int 0
"fuel dealers" int 0
"office supplies and stationery stores" int 0
"automobile dealers" int 0
"retail trade, ex auto" int 172890
"gift, novelty, and souvenir stores" int 0
"retail trade and food services, ex auto" int 0
"retail trade" int 235509
"building materials and garden supplies dealers" int 19545
"supermarkets and other grocery (except convenience) stores" int 0
"book stores" int 0
"furniture stores" int 0
"gafo" int 0
"electronic shopping and mail-order houses" int 0
"clothing stores" int 22622
"used car dealers" int 0
"full service restaurants" int 0
"warehouse clubs and superstores" int 0
"hobby, toy, and game stores" int 0
"nonstore retailers" int 0
"all other home furnishings stores" int 0
"retail trade and food services" int 0
"paint and wallpaper stores" int 0
"women's clothing stores" int 0
"computer and software stores" int 0
"jewelry stores" int 0
"non-discount department stores" int 0
Key Type Example Value Comment
"month name" str "Jan"
"month" int 1
"index" int 1
"period" str "Jan1992"
"year" int 1992
Key Type Example Value Comment
"floor covering stores" int 728
"grocery stores" int 27306
"limited service eating places" int 6438
"shoe stores" int 1206
"health and personal care stores" int 7258
"miscellaneous store retailers" int 3642
"sporting goods stores" int 972
"family clothing stores" int 1649
"drinking places" int 1049
"other general merchandise stores" int 4690
"home furnishings stores" int 1454
"hardware stores" int 842
"auto and other motor vehicles" int 26788
"food and beverage stores" int 29589
"men's clothing stores" int 701
"sporting goods, hobby, book, and music stores" int 3439
"used merchandise stores" int 371
"furniture, home furn, electronics, and appliance stores" int 7087
"gasoline stations" int 12099
"other clothing stores" int 0
"office supplies, stationery, and gift stores" int 1446
"discount department stores" int 5601
"building supplies dealers" int 7637
"automotive parts and tire stores" int 3023
"general merchandise stores" int 14996
"motor vehicle and parts dealers" int 29811
"all other merchandise stores" int 2111
"new car dealers" int 24056
"household appliance stores" int 601
"electronics and appliance stores" int 3241
"non-leased department stores" int 10306
"furniture and home furnishings stores" int 3846
"all department stores" int 10560
"pharmacies and drug stores" int 6358
"radio, TV, and electronics stores" int 1541
"food services and drinking places" int 15693
"appliances and other electronics stores" int 2142
"beer, wine, and liquor stores" int 1509
"fuel dealers" int 1916
"office supplies and stationery stores" int 788
"automobile dealers" int 25800
"retail trade, ex auto" int 100872
"gift, novelty, and souvenir stores" int 658
"retail trade and food services, ex auto" int 116565
"retail trade" int 130683
"building materials and garden supplies dealers" int 8964
"supermarkets and other grocery (except convenience) stores" int 0
"book stores" int 790
"furniture stores" int 2392
"gafo" int 33906
"electronic shopping and mail-order houses" int 2692
"clothing stores" int 4852
"used car dealers" int 1744
"full service restaurants" int 6887
"warehouse clubs and superstores" int 2579
"hobby, toy, and game stores" int 620
"nonstore retailers" int 6860
"all other home furnishings stores" int 0
"retail trade and food services" int 146376
"paint and wallpaper stores" int 0
"women's clothing stores" int 1873
"computer and software stores" int 933
"jewelry stores" int 796
"non-discount department stores" int 4959
Key Type Example Value Comment
"floor covering stores" int 0
"grocery stores" int 0
"limited service eating places" int 0
"shoe stores" int 0
"health and personal care stores" int 0
"miscellaneous store retailers" int 0
"sporting goods stores" int 0
"family clothing stores" int 0
"drinking places" int 0
"other general merchandise stores" int 0
"home furnishings stores" int 0
"hardware stores" int 0
"auto and other motor vehicles" int 0
"food and beverage stores" float 0.91
"men's clothing stores" int 0
"sporting goods, hobby, book, and music stores" int 0
"used merchandise stores" int 0
"furniture, home furn, electronics, and appliance stores" float 1.95
"gasoline stations" int 0
"other clothing stores" int 0
"office supplies, stationery, and gift stores" int 0
"discount department stores" int 0
"building supplies dealers" int 0
"automotive parts and tire stores" int 0
"general merchandise stores" float 2.92
"motor vehicle and parts dealers" float 2.1
"all other merchandise stores" int 0
"new car dealers" int 0
"household appliance stores" int 0
"electronics and appliance stores" int 0
"non-leased department stores" float 3.28
"furniture and home furnishings stores" int 0
"all department stores" int 0
"pharmacies and drug stores" int 0
"radio, TV, and electronics stores" int 0
"food services and drinking places" int 0
"appliances and other electronics stores" int 0
"beer, wine, and liquor stores" int 0
"fuel dealers" int 0
"office supplies and stationery stores" int 0
"automobile dealers" int 0
"retail trade, ex auto" float 1.71
"gift, novelty, and souvenir stores" int 0
"retail trade and food services, ex auto" int 0
"retail trade" float 1.8
"building materials and garden supplies dealers" float 2.18
"supermarkets and other grocery (except convenience) stores" int 0
"book stores" int 0
"furniture stores" int 0
"gafo" int 0
"electronic shopping and mail-order houses" int 0
"clothing stores" float 3.26
"used car dealers" int 0
"full service restaurants" int 0
"warehouse clubs and superstores" int 0
"hobby, toy, and game stores" int 0
"nonstore retailers" int 0
"all other home furnishings stores" int 0
"retail trade and food services" int 0
"paint and wallpaper stores" int 0
"women's clothing stores" int 0
"computer and software stores" int 0
"jewelry stores" int 0
"non-discount department stores" int 0
Key Type Example Value Comment
"inventories" dict { }
"ratio" dict { }
"sales" dict { }
Key Type Example Value Comment
"data" dict { } Numbers represent millions of dollars
"time" dict { }

Downloads

Download all of the following files.

Usage

This library has 1 function you can use.
import retail_services
list_of_report = retail_services.get_report()
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 retail_services
# These may be slow!
list_of_report = retail_services.get_report(test=True)

Documentation

 retail_services.get_report(test=False)

Returns sales, inventory, and ratio data for every month.