Editing Comma-separated values (section)

==Applications==
CSV is a common [[data exchange]] format that is widely supported by consumer, business, and scientific applications. Among its most common uses is moving tabular data<ref>{{cite web|url=https://datahub.io/docs/data-packages/csv|title=CSV - Comma Separated Values|access-date=2017-12-02|archive-date=2021-03-07|archive-url=https://web.archive.org/web/20210307221025/https://datahub.io/docs/data-packages/csv|url-status=live}}</ref><ref>{{cite web | url=http://www.csvreader.com/csv_format.php | title=CSV Files | access-date=June 4, 2014 | archive-date=April 30, 2021 | archive-url=https://web.archive.org/web/20210430225021/https://www.csvreader.com/csv_format.php | url-status=live }}</ref> between programs that natively operate on incompatible (often [[proprietary format|proprietary]] or undocumented) formats.<ref name="rfc4180">{{cite IETF |title=Common Format and MIME Type for CSV Files |last=Shafranovich |first=Y. |publisher=[[Internet Engineering Task Force|IETF]] |date=October 2005 |rfc=4180 |page=1 }}</ref> For example, a user may need to transfer information from a database program that stores data in a proprietary format, to a [[spreadsheet]] that uses a completely different format. Most [[Database|database programs]] can export data as CSV. Most spreadsheet programs can read CSV data, allowing CSV to be used as an intermediate format when transferring data from a database to a spreadsheet. Every major [[E-commerce|ecommerce]] platform provides support for exporting data as a CSV file.<ref>{{Cite web |title=CSV Supported Ecommerce Platforms |url=https://www.rfmcalc.com/supported-platforms/ |access-date=2025-03-09 | website=RFM Calc }}</ref>

CSV is also used for storing data. Common data science tools such as [[Pandas (software)|Pandas]] include the option to export data to CSV for long-term storage.<ref>{{Cite web |title=pandas.DataFrame.to_csv — pandas 2.0.3 documentation |url=https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html |access-date=2023-08-16 |website=pandas.pydata.org}}</ref> Benefits of CSV for data storage include the simplicity of CSV makes parsing and creating CSV files easy to implement and fast compared to other data formats, human readability making editing or fixing data simpler,<ref>{{Cite web |date=2021-09-15 |title=CSV Format: History, Advantages and Why It Is Still Popular |url=https://bytescout.com/blog/csv-format-history-advantages.html |access-date=2023-08-16 |website=ByteScout |language=en-US}}</ref> and high compressibility leading to smaller data files.<ref>{{Cite web |date=2020-07-23 |title=Comparison of different file formats in Big Data |url=https://www.adaltas.com/en/2020/07/23/benchmark-study-of-different-file-format/ |access-date=2023-08-16 |website=www.adaltas.com |language=en}}</ref> Alternatively, CSV does not support more complex data relations and makes no distinction between null and empty values, and in applications where these features are needed other formats are preferred.

More than 200 local, regional, and national data portals, such as those of the [[UK government]] and the [[European Commission]], use CSV files with standardized [[Database catalog|data catalogs]].<ref>{{Cite book |last1=Mahmud |first1=S M Hasan |last2=Hossin |first2=Md Altab |last3=Jahan |first3=Hosney |last4=Noori |first4=Sheak Rashed Haider |last5=Bhuiyan |first5=Touhid |chapter=CSV-ANNOTATE: Generate annotated tables from CSV file |title=2018 International Conference on Artificial Intelligence and Big Data (ICAIBD) |chapter-url=https://ieeexplore.ieee.org/document/8396169 |publisher=IEEE |publication-date=2018 |pages=71–75 |doi=10.1109/ICAIBD.2018.8396169 |isbn=978-1-5386-6987-7}}</ref>