A list of Starbucks locations, scraped from the web in 2017
chrismeller.github.com-starbucks-2.1.1. Modified 2021-04-02T14:52:09
Resources | Packages | Documentation| Contacts| References| Data Dictionary
Resources
- all_starbucks. List of Starbucks locations.
- us_starbucks. Geo file of starbucks in the US
Documentation
Starbucks locations scraped from the Starbucks website by Chris Meller. From the original README:
Contains a single commit for each day that batches up all changes to the
Starbucks dataset as a CSV file.
Deletes will obviously be stores that seem to have closed and adds are new
stores that have opened.
This is a work in progress. There is additional data that needs to be
included in this dataset and it needs to get properly automated so we hav e
a regular commit daily at a particular time.
This dataset release re-geocodes all of the addresses, for the us_starbucks
dataset. The original datafile has lat and lon values truncated to 2 decimal
places, about 1km in North America. The re-geocoded addressss are much more
precise.
Documentation Links
- Documentation Page
- Source Github page for the datafile source
Contacts
- Wrangler
Data Dictionary
all_starbucks | us_starbucksall_starbucks
| Column Name | Data Type | Description |
|---|---|---|
| id | string | |
| starbucksid | integer | |
| name | text | |
| brandname | string | |
| storenumber | string | |
| phonenumber | string | |
| ownershiptype | string | |
| street1 | text | |
| street2 | text | |
| street3 | text | |
| city | text | |
| countrysubdivisioncode | string | |
| countrycode | string | |
| postalcode | string | |
| longitude | number | |
| latitude | number | |
| timezoneoffset | integer | |
| timezoneid | string | |
| timezoneolsonid | string | |
| firstseen | datetime | |
| lastseen | datetime |
us_starbucks
| Column Name | Data Type | Description |
|---|---|---|
| id | string | |
| starbucksid | integer | |
| name | text | |
| brandname | string | |
| storenumber | string | |
| phonenumber | string | |
| ownershiptype | string | |
| street1 | string | |
| street2 | string | |
| street3 | string | |
| city | string | |
| countrysubdivisioncode | string | |
| countrycode | string | |
| postalcode | integer | |
| source_longitude | number | |
| source_latitude | number | |
| timezoneoffset | integer | |
| timezoneid | string | |
| timezoneolsonid | string | |
| firstseen | datetime | |
| lastseen | datetime | |
| geocoder | string | |
| lat | number | |
| lon | number | |
| geoid | string | |
| geometry | string |
References
Urls used in the creation of this data package.
- censusgeo://2019/5/US/state. US States
- data/geocodes.csv. geocoded addresses
Packages
- s3 s3://radius.civicknowledge.com/chrismeller.github.com-starbucks-2.1.1.csv
- csv http://s3.amazonaws.com/radius.civicknowledge.com/chrismeller.github.com-starbucks-2.1.1.csv
- source https://github.com/metatab-packages/chrismeller.github.com-starbucks.git
Accessing Data in Vanilla Pandas
import pandas as pd
all_starbucks_df = pd.read_csv('http://s3.amazonaws.com/radius.civicknowledge.com/chrismeller.github.com-starbucks-2.1.1/data/all_starbucks.csv')
us_starbucks_df = pd.read_csv('http://s3.amazonaws.com/radius.civicknowledge.com/chrismeller.github.com-starbucks-2.1.1/data/us_starbucks.csv')Accessing Package in Metapack
import metapack as mp
pkg = mp.open_package('http://s3.amazonaws.com/radius.civicknowledge.com/chrismeller.github.com-starbucks-2.1.1.csv')
# Create Dataframes
all_starbucks_df = pkg.resource('all_starbucks').dataframe()
us_starbucks_gdf = pkg.resource('us_starbucks').geoframe()