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CSV is a common file format used regularly in data analytics and BI tools. It is important to get a good understanding of the CSV format to better handle the data you work with. This article runs through some of the nuances of working with CSV files. Feel free to share your own tips in the comments section at the end.
A comma-separated values (CSV) file is a text file that uses a comma to separate values. The use of the comma as a field separator is the source of the name for this file format. The term "CSV" also denotes several closely related, but not fully standardized, formats that use semicolons or other field-separating characters to separate fields. These include tab-separate values and space-separation files that use other field delimiter characters such as semicolons or tabs to separate field data.
The loose terminology can cause problems in data exchange, especially in many European locales where semicolons are often used instead of commas in many locales to use a decimal separator and, possibly, the period as a grouping character.
Basic rules for the creation and formatting of CSV files are:
CSV is a common data exchange format. It is widely supported by consumer, business, and scientific applications. Among its most common uses is moving tabular data between programs that natively operate on incompatible (often proprietary or undocumented) formats.
CSV files helps the users to save information in a structured text format.
Most of the databases support CSV file format for the import or export of data from the databases.
You can store, transfer or analyse data in .CSV files using BI RPA tools with easy setup and intuitive use for multiple CSV files.
Data visualization applications are also compatible with CSV files, so anyone can have an Intuitive Natural Language Interface to CSV files to inform and speed business decisions with significant benefits for anyone.
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