|xlsx||2.33 MB||Apache-2.0||9 Years||24 Mar 2022|
|csv-parse||268.68 kB||MIT||10 Years||25 Aug 2023|
|csv||399.84 kB||MIT||12 Years||25 Aug 2023|
|papaparse||44.81 kB||MIT||9 Years||23 Mar 2023|
|exceljs||5.23 MB||MIT||8 Years||21 Aug 2021|
|fast-csv||2.84 kB||MIT||11 Years||4 Dec 2020|
|csv-parser||8.55 kB||MIT||9 Years||3 Dec 2020|
|json2csv||14.29 kB||MIT||11 Years||21 Jan 2023|
|csvtojson||1.09 MB||MIT||10 Years||26 Jun 2019|
|csv-stringify||193.2 kB||MIT||10 Years||25 Aug 2023|
|d3-dsv||11.78 kB||ISC||8 Years||5 Jun 2021|
|neat-csv||2.08 kB||MIT||8 Years||18 Oct 2021|
|stream-transform||234.77 kB||MIT||9 Years||25 Aug 2023|
|react-csv||11.52 kB||MIT||7 Years||18 Jan 2022|
|json-2-csv||24.04 kB||MIT||9 Years||3 Aug 2023|
CSV (Comma Separated Values) libraries are primarily used when there is a need to work with CSV formatted data. This data format is widespread, particularly in areas where data extraction and data analysis is carried out, including but not limited to data science, machine learning, and business analytics.
These libraries become very useful in certain situations:
Data Import: CSV libraries are used to parse CSV files and turn them into usable data structures in a program. This is often used to import data from an external source into a system.
Data export: When there is a need to export data from a system, one popular method used is to create CSV files. This allows the data to be used in many different environments with minimal overhead.
Data Manipulation: If there is a requirement to modify, delete or alter CSV data, CSV libraries provide helpful methods to do this with ease.
Data Analysis: CSV libraries are useful when there is a need to do analysis with CSV data - be it basic aggregations, sorting, or more complex tasks.
CSV libraries come with a set of core functionalities that make working with this data format more straightforward:
Parsing: All CSV libraries provide functionality to parse CSV data. This means that they convert CSV formatted data into a data form that is easier to work with in a programming environment.
Stringifying: In addition to parsing, most CSV libraries also provide capability to convert data structures back into a CSV format.
Reading and Writing Files: Most CSV libraries can read CSV data from a local file, and write data into local file in CSV format.
Stream Support: Another feature common in CSV libraries is CSV stream support. This allows for lower memory usage when handling larger files.
Support for Options: Most CSV libraries provide support for various options - like delimiter choice, quote characters, escape characters, etc. This allows the user to work with various kinds of CSV data.
Error Handling: Most CSV libraries provide errors handlers for common issues such as incorrect data format, file not found etc.
Here are some common gotchas/pitfalls to look out for when working with CSV Libraries:
Delimiter Issues: It's important to use the correct delimiter when parsing CSV files. Some files might use semicolons, tabs, or other characters, which can lead to incorrect parsing.
Escape Characters and Special Characters: The handling of escape and special characters can cause issues. Not all libraries handle these the same way, which can lead to incorrect parsing or writing of CSV data.
Large Files: Working with large data files can sometimes prove to be a challenge. Issues can arise around performance and memory usage when handling larger files.
Encoding Issues: Different systems can sometimes use different encodings, so one needs to pay attention to ensure a CSV file is read or written in the correct encoding.
Header Mismatch: If the number of headers doesn't match the number of data elements in a row, parsing might fail or yield incorrect results.