csv — The Logtalk Handbook v3.47.0 documentation

# `csv`

The `csv` library provides predicates for reading and writing CSV files:

https://www.rfc-editor.org/rfc/rfc4180.txt

The main object, `csv/3`, is a parametric object allowing passing options for the handling of the header of the file, the fields separator, and the handling of double-quoted fields. The `cvs` object extends the `csv/3` parametric object using default option values.

The library also include predicates to guess the separator and guess the number of columns in a given CSV file.

Files can be read into a list of rows (with each row being represented by a list of fields) or asserted using a user-defined dynamic predicate.

Data can be saved to a CSV file by providing the object and predicate for accessing the data plus the name of the destination file.

## API documentation

Open the ../../docs/library_index.html#csv link in a web browser.

## Loading

To load all entities in this library, load the `loader.lgt` file:

    | ?- logtalk_load(csv(loader)).

## Testing

To test this library predicates, load the `tester.lgt` file:

    | ?- logtalk_load(csv(tester)).

## Usage

The `csv(Header, Separator, IgnoreQuotes)` parametric object allows passing the following options:

1. `Header`: possible values are `missing`, `skip`, and `keep`.

2. `Separator`: possible values are `comma`, `tab`, `semicolon`, and `colon`.

3. `IgnoreQuotes`: possible values are `true` to ignore double quotes surrounding field data and `false` to preserve the double quotes.

The `csv` object uses the default values `keep`, `comma`, and `false`.

When writing CSV files, set the quoted fields option to `false` to write all non-numeric fields double-quoted (i.e. escaped).

The library objects can also be used to guess the separator used in a CSV file if necessary. For example:

    | ?- csv::guess_separator('test_files/crlf_ending.csv', Separator).
    Is this the proper reading of a line of this file (y/n)? [aaa,bb,ccc]
    |> y.

    Separator = comma ?

This information can then be used to read the CSV file returning a list of rows:

    | ?- csv(keep, comma, true)::read_file('test_files/crlf_ending.csv', Rows).

    Rows = [[aaa,bbb,ccc],[zzz,yyy,xxx]] ?

Alternatively, The CSV data can be saved using a public and dynamic object predicate. For example:

    | ?- csv(keep, comma, true)::read_file('test_files/crlf_ending.csv', user, p/3).
    ** yes

    | ?-  p(A,B,C).

    A = aaa
    B = bbb
    C = ccc ? ;

    A = zzz
    B = yyy
    C = xxx

Given a predicate representing a table, the predicate data can be written to a file:

    | ?- csv(keep, comma, true)::write_file('output.csv', user, p/3).
    ** yes

leaving the content just as the original file thanks to the use of `true` for the `IgnoreQuotes` option:

    aaa,bbb,ccc
    zzz,yyy,xxx

Otherwise:

    | ?- csv(keep, comma, false)::write_file('output.csv', user, p/3).
    ** yes

results in the following file content:

    "aaa","bbb","ccc"
    "zzz","yyy","xxx"

The `guess_arity/2` method, to identify the arity, i. e. the number of fields or columns per record in a given CSV file, for example:

    | ?- csv(keep, comma, false)::guess_arity('test_files/crlf_ending.csv', Arity).
    Is this the proper reading of a line of this file (y/n)? [aaa,bbb,ccc]
    |> y.

    Arity = 3