# ftcsv
ftcsv, a fairly fast csv library written in pure Lua. It's been tested with LuaJIT 2.0/2.1 and Lua 5.2

It works well for CSVs that can easily be fully loaded into memory (easily up to a hundred MB). Currently, there isn't a "large" file mode with proper readers and writers for ingesting CSVs in bulk with a fixed amount of memory. It correctly handles both `\n` (LF) and `\r\n` (CRLF) line endings (ie it should work with Windows and Mac/Linux line endings) and has UTF-8 support.



## Installing
You can either grab `ftcsv.lua` from here or install via luarocks:

```
luarocks install ftcsv
```


## Parsing
###`ftcsv.parse(fileName, delimiter [, options])`

ftcsv will load the entire csv file into memory, then parse it in one go, returning a lua table with the parsed data. It has only two required parameters - a file name and delimiter (limited to one character). A few optional parameters can be passed in via a table (examples below).

Just loading a csv file:
```lua
local ftcsv = require('ftcsv')
local zipcodes = ftcsv.parse("free-zipcode-database.csv", ",")
```

### Options
The following are optional parameters passed in via the third argument as a table. For example if you wanted to `loadFromString` and not use `headers`, you could use the following:
```lua
ftcsv.parse("apple,banana,carrot", ",", {loadFromString=true, headers=false})
```
 - `loadFromString`

 	If you want to load a csv from a string instead of a file, set `loadFromString` to `true` (default: `false`)
 	```lua
	ftcsv.parse("a,b,c\r\n1,2,3", ",", {loadFromString=true})
 	```

 - `rename`

 	If you want to rename a field, you can set `rename` to change the field names. The below example will change the headers from `a,b,c` to `d,e,f`

 	Note: You can rename two fields to the same value, ftcsv will keep the field that appears latest in the line.

 	```lua
 	local options = {loadFromString=true, rename={["a"] = "d", ["b"] = "e", ["c"] = "f"}}
	local actual = ftcsv.parse("a,b,c\r\napple,banana,carrot", ",", options)
 	```

 - `fieldsToKeep`

 	If you only want to keep certain fields from the CSV, send them in as a table-list and it should parse a little faster and use less memory.

 	Note: If you want to keep a renamed field, put the new name of the field in `fieldsToKeep`:

 	```lua
	local options = {loadFromString=true, fieldsToKeep={"a","f"}, rename={["c"] = "f"}}
	local actual = ftcsv.parse("a,b,c\r\napple,banana,carrot\r\n", ",", options)
 	```

 - `headers`

 	Set `headers` to `false` if the file you are reading doesn't have any headers. This will cause ftcsv to create indexed tables rather than a key-value tables for the output.

 	```lua
	local options = {loadFromString=true, headers=false}
	local actual = ftcsv.parse("apple>banana>carrot\ndiamond>emerald>pearl", ">", options)
 	```

 	Note: Header-less files can still use the `rename` option and after a field has been renamed, it can specified as a field to keep. The `rename` syntax changes a little bit:

 	```lua
	local options = {loadFromString=true, headers=false, rename={"a","b","c"}, fieldsToKeep={"a","b"}}
	local actual = ftcsv.parse("apple>banana>carrot\ndiamond>emerald>pearl", ">", options)
 	```

 	In the above example, the first field becomes 'a', the second field becomes 'b' and so on.

For all tested examples, take a look in /spec/feature_spec.lua


## Encoding
###`ftcsv.encode(inputTable, delimiter[, options])`
ftcsv can also take a lua table and turn it into a text string to be written to a file. It has two required parameters, an inputTable and a delimiter. You can use it to write out a file like this:
```lua
local fileOutput = ftcsv.encode(users, ",")
local file = assert(io.open("ALLUSERS.csv", "w"))
file:write(fileOutput)
file:close()
```

### Options
 - `fieldsToKeep`

	if `fieldsToKeep` is set in the encode process, only the fields specified will be written out to a file.

	```lua
	local output = ftcsv.encode(everyUser, ",", {fieldsToKeep={"Name", "Phone", "City"}})
	```



## Performance
I did some basic testing and found that in lua, if you want to iterate over a string character-by-character and look for single chars, `string.byte` performs better than `string.sub`. As such, ftcsv iterates over the whole file and does byte compares to find quotes and delimiters and then generates a table from it. If you have thoughts on how to improve performance (either big picture or specifically within the code), create a GitHub issue - I'd love to hear about it!



## Contributing
Feel free to create a new issue for any bugs you've found or help you need. If you want to contribute back to the project please do the following:
 1. Fork the repo
 2. Create a new branch
 3. Push your changes to the branch
 4. Run the test suite and make sure it still works
 5. Submit a pull request
 6. ???
 7. Enjoy the changes made to the repo!



## Licenses
 - The main library is licensed under the MIT License. Feel free to use it!
 - Some of the test CSVs are from [csv-spectrum](https://github.com/maxogden/csv-spectrum) (BSD-2-Clause) which includes some from [csvkit](https://github.com/wireservice/csvkit) (MIT License)