CSV.jl

CSV.jl reads and writes comma-separated and other delimited text data. Its readers and writer implement the Tables.jl interfaces, so CSV data can move between Julia table packages without a CSV-specific adapter.

Installation

Install the registered release in the Julia REPL:

] add CSV

CSV.jl 1.0 requires Julia 1.10 or later. Read Migrating to 1.0 before you update an application from CSV.jl 0.10.

First read and write

using CSV

input = IOBuffer("name,score\nAda,9.5\nGrace,10.0\n")
file = CSV.File(input)

(names(file), String.(file.name), collect(file.score))

([:name, :score], ["Ada", "Grace"], [9.5, 10.0])

CSV.File is a Tables.jl table. A table sink can consume it directly. The writer accepts any Tables.jl table:

output = IOBuffer()
CSV.write(output, (name=["Ada", "Grace"], score=[9.5, 10.0]))
String(take!(output))

"name,score\nAda,9.5\nGrace,10.0\n"

Select a reader

API	Use it when	Materialization model
`CSV.File`	You need a normal in-memory table	Index once, then parse columns
`CSV.read`	You want to hand the parsed columns to a sink	Same parse as `CSV.File`; marks columns as safe to take
`CSV.lazy`	You need a fast first look or sparse cell access	Index now; parse each accessed cell later
`CSV.Rows`	You process rows once and do not need columns	Indexes the source; materializes cells on access
`CSV.Chunks`	You process a large table in bounded batches	Indexes once; parses one stable-schema batch at a time

CSV.lazy, CSV.Rows, and CSV.Chunks do not stream an unbounded input. They retain the source bytes and a structural index. See Input and memory behavior for the exact source rules.

Data model

CSV.jl builds one quote-aware structural index. It then parses each selected column with a type-specialized loop. Parallel execution does not change row order, limit results, or output bytes.

Date-time columns prefer Timestamp{Nanosecond} from Durations.jl, which uses Dates.Timestamp when the standard library provides it. Wider dates use microseconds if all values fit exactly; otherwise the column stays text. Request Dates.DateTime with types when a consumer needs it.

Text columns use DataStrings.DataString by default. Short values are stored in the value; longer values live in buffers the column owns, so an eager table never refers to its source. Convert with String(value) when a standalone String is required, or pass stringtype=String to a reader.

Parse problems are structured data. A read prints one summary warning and keeps the problems; call CSV.problems(file) to inspect them, set on_error=:collect to skip the warning, or on_error=:error when a parse problem must stop the read with a CSV.ParseError.