csv-split-easy^7.2.1

Installation

Quick Take

Examples

• Leave single decimal place numbers unpadded
• Unescape doubled-up double quotes
• Keep the thousand separators in numbers
• opts.delimiter
• Skip blank rows
• Convert continental number style into UK style

Purpose

Split a string representing a CSV file into an array of arrays, so that you can traverse it later.

Acceptance Criteria:

• It should accept CSV’s with or without a header row
• Header row might have different amount of columns than the rest of the rows
• Content (not header) rows might be offset and have different amount of columns from the rest
• There can be various line break types (\n, \r, \n\r or even \n\n\n\n\n\n\r\r\r)
• It should ignore any values wrapped with double quotes
• It should interpret commas as part of the value if it is wrapped in double quotes
• It should accept empty fields and output them as empty strings
• It should automatically detect (dot/comma) and remove thousand separators from digit-only cells
• Minimal dependencies and 100% unit test code coverage in all ways: per-branch, per-statement, per-function and per-line.

Outside of the scope:

• Trimming the values of leading and trailing empty space. Just use String.prototype.trim()
• Parsing numeric values. Parse them yourself. It’s outside of the scope of this program.
• Sorting rows of double-entry, accounting CSV’s. See csv-sort.

API — splitEasy()

The main function splitEasy() is imported like this:

It’s a function which takes two input arguments:

The optional options object has the following shape:

Here are all defaults in one place for copying:

Function returns an array of arrays (each depicting a row). When there’s nothing given, returns [['']]

There’s always one array within the main array and there’s always something there, at least an empty string.

API — defaults

You can import defaults:

It's a plain object:

The main function calculates the options to be used by merging the options you passed with these defaults.

API — version

You can import version:

The algorithm

CSV files, especially accounting-ones, are different from just any files. The assumption here is that empty rows are not wanted in the parsed arrays. It means, conventional string splitting libraries would be inefficient here because after splitting, you’d have to clean up any empty rows.

The second requirement is that any of the column values in CSV can be wrapped with double quotes. We have to support mixed, wrapped and not wrapped-value CSV’s because Metro bank used to produce these when we banked with them back in 2015.

The third requirement is that any of the values can be wrapped with double quotes and have commas within as values.

The requirements mentioned above pretty much rule out the conventional regex-based split algorithms. You can just split by /\r?\n/ but later you’ll need to clean up possible empty rows. You can’t string.split each row by comma because that comma might be a value, you need to check for wrapping double quotes first!

So, the best algorithm is a single for-loop traversal on the input string, detecting and array.pushing the values one by one. It worked very well on email-comb where we remove unused CSS from an HTML template within around 2.5 times more characters “travelled” than there are in the file. Traversing as a string also worked well on html-img-alt which needs only a single traversal through the string to fix all the img tag alt attributes and clean all the crap in/around them.

Changelog