Abstract
The Adapter Python IO software, in short Adapter or the Adapter software, encapsulates certain Python IO capabilities used for loading in and writing out data when performing analytical Python code runs. More specificcally, it provides a Python API to load data tables from various formats such as XLSX (MS Excel), CSV, and database, into Python code as Pandas DataFrames, as well as to write out tables into a database or CSV files. The Adapter software standardizes a way to point the code to one or multiple input files of one or multiple formats. Therefore, its main feature is the ability to convert data tables identified in one main and, optionally, one or more additional input files, into database tables and Pandas DataFrames for downstream usage in any compatible software. In addition to the loading capability, an instance of the Adapter IO object has the capability to write data out. If the write capability is invoked, all loaded tables are written as either a single database or a set of CSV files, or both, to a location specified in the dedicated input table.
- Developers:
-
Grahovac, Milica [1] ; Smires, Youness [1] ; Burke, Thomas [1] ; Coughlin, Katie [1] ; Ganeshalingam, Mohan [1] ; Mathur, Akhil [1] ; Neill, Evan [1] ; Sharma, Akshay [1]
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Release Date:
- 2021-04-20
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC02-05CH11231
- Code ID:
- 56244
- Site Accession Number:
- 2021-051
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Country of Origin:
- United States
Citation Formats
Grahovac, Milica, Smires, Youness B., Burke, Thomas, Coughlin, Katie, Ganeshalingam, Mohan, Mathur, Akhil, Neill, Evan, and Sharma, Akshay.
Adapter Python IO (Adapter) v1.0.
Computer Software.
https://github.com/LBNL-ETA/Adapter.
USDOE.
20 Apr. 2021.
Web.
doi:10.11578/dc.20210514.3.
Grahovac, Milica, Smires, Youness B., Burke, Thomas, Coughlin, Katie, Ganeshalingam, Mohan, Mathur, Akhil, Neill, Evan, & Sharma, Akshay.
(2021, April 20).
Adapter Python IO (Adapter) v1.0.
[Computer software].
https://github.com/LBNL-ETA/Adapter.
https://doi.org/10.11578/dc.20210514.3.
Grahovac, Milica, Smires, Youness B., Burke, Thomas, Coughlin, Katie, Ganeshalingam, Mohan, Mathur, Akhil, Neill, Evan, and Sharma, Akshay.
"Adapter Python IO (Adapter) v1.0." Computer software.
April 20, 2021.
https://github.com/LBNL-ETA/Adapter.
https://doi.org/10.11578/dc.20210514.3.
@misc{
doecode_56244,
title = {Adapter Python IO (Adapter) v1.0},
author = {Grahovac, Milica and Smires, Youness B. and Burke, Thomas and Coughlin, Katie and Ganeshalingam, Mohan and Mathur, Akhil and Neill, Evan and Sharma, Akshay},
abstractNote = {The Adapter Python IO software, in short Adapter or the Adapter software, encapsulates certain Python IO capabilities used for loading in and writing out data when performing analytical Python code runs. More specificcally, it provides a Python API to load data tables from various formats such as XLSX (MS Excel), CSV, and database, into Python code as Pandas DataFrames, as well as to write out tables into a database or CSV files. The Adapter software standardizes a way to point the code to one or multiple input files of one or multiple formats. Therefore, its main feature is the ability to convert data tables identified in one main and, optionally, one or more additional input files, into database tables and Pandas DataFrames for downstream usage in any compatible software. In addition to the loading capability, an instance of the Adapter IO object has the capability to write data out. If the write capability is invoked, all loaded tables are written as either a single database or a set of CSV files, or both, to a location specified in the dedicated input table.},
doi = {10.11578/dc.20210514.3},
url = {https://doi.org/10.11578/dc.20210514.3},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20210514.3}},
year = {2021},
month = {apr}
}