Python version of the R arcos package
Note: as of 5/20/2021 you may need to run
from arcospy import arcospy. You can then access functions as
arcospy.county_raw() among others. Will attempt to resolve this in the coming weeks.
arcospy, the python version of the R
arcos package maintained by The Washington Post.
arcospy is the result of a R-to-python translation project carried out at the University of Maryland in the Fall of 2019. The project was motivated a Washington Post data-driven story on a large pain pill database recently made publicly available.
arcospy module was built to offer the exact same functionality as
arcos, with the only difference being the ability to run the API calls in
python! All of the commands in
arcospy inherit the names from the original commands in
arcospy act as wrappers for the DEA ARCOS dataset.
arcospy is hosted on PyPI and is
pip installable. To install
arcospy on your machine, start a new terminal and run the following commands:
$ pip install arcospy
Updates (will be posted periodically):
- 5/20/2021: Changes in API payload required a minor update to certain raw data commands.
- 9/14/2020: Updated docs to reflect forthcoming publication of
- 8/1/2020: Renamed demos to docs for development consistency. Added another example notebook demonstrating acquisition and manipulation of pharmacy-level data. Planning gathering of additional ARCOS reports.
- 3/18/2020: Added four new commands for business-level data, as well as updating documentation to reflect additional years now present in the data (up to 2014).
- 3/1/2020: Re-organized repository for readability. Updated testing documents. Fixed two small parameter issues with state-level query commands. 1.0.8 live and stable.
- 1/22/2020: Carried out PEP8 styling for commands, added help information, and package header. Re-published package as 1.0.6 as the package is now considered stable.
- 1/14/2020: Reformatted
README.mdto provide more specific headers, installation instructions, requirements, and additional information.
- 12/10/2019: added a new folder called demos. Inculdes a basic getting started guide as well as an introduction to making the data spatial.
Data can be gathered at the pharmacy, distributor, county, or state as the geographic unit of analysis. Depending on the geographic level, there may be raw, summarized, or supplemental data available. For example, the
county_raw() command returns each individual ARCOS record for a given county from 2006 to 2014. However, the
summarized_county_annual() command returns the annual summarized totals for a given county for each year of 2006 to 2014.
Contributions are welcome to both
arcospy. For major contributions, please fork the master
arcospy branch and then open a pull request with the suggested changes. We will then review the change and determine if there is a generalizable solution to both
Python. If there is no generalizable solution, we will still strive to make your contribution visible on the respective Github page.
Improvements to the documentation for
arcospy are welcome. As stated above, we will try to generalize all contributions to both packages. For example, if the wording around a specific command is unclear, we can improve the wording in both packages. Additionally, if there are features of a command that you believe should be included in the primary documentation, please let us know so we can improve the user experience.
Presently, the core functionality of the API is maintained by the Data Reporting Team at The Washington Post. There is ample room for users to suggest functions that can be added to
arcospy. For example, users might suggest functions that download national or regional sets of data by looping existing commands.
Trackable issue pages are available on both the master
arcospy Github pages. Issues may be related to anything from malfunctioning commands to inconsistent data. We encourage an active discussion and hope to readily address any errors. We recommend that when submitting an issue users provide specific tags and examples of the aberrant behavior. If you are able to solve an issue with the code independently, please open a pull request with the corrected code and a short explanation as to the bug fix.
Disclaimer: please note that the author of this package, Jeff Sauer, is not affiliated with the Washington Post in any official capacity.
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