CSV.jl Documentation

CSV.File(source; kwargs...) => CSV.File

Read a UTF-8 CSV input (a filename given as a String or FilePaths.jl type, or any other IO source), returning a CSV.File object.

Opens the file and uses passed arguments to detect the number of columns and column types, unless column types are provided manually via the types keyword argument. Note that passing column types manually can increase performance and reduce the memory use for each column type provided (column types can be given as a Vector for all columns, or specified per column via name or index in a Dict). For text encodings other than UTF-8, see the StringEncodings.jl package for re-encoding a file or IO stream. The returned CSV.File object supports the Tables.jl interface and can iterate CSV.Rows. CSV.Row supports propertynames and getproperty to access individual row values. CSV.File also supports entire column access like a DataFrame via direct property access on the file object, like f = CSV.File(file); f.col1. Note that duplicate column names will be detected and adjusted to ensure uniqueness (duplicate column name a will become a_1). For example, one could iterate over a csv file with column names a, b, and c by doing:

for row in CSV.File(file)
    println("a=$(row.a), b=$(row.b), c=$(row.c)")
end

By supporting the Tables.jl interface, a CSV.File can also be a table input to any other table sink function. Like:

# materialize a csv file as a DataFrame, without copying columns from CSV.File; these columns are read-only
df = CSV.File(file) |> DataFrame!

# load a csv file directly into an sqlite database table
db = SQLite.DB()
tbl = CSV.File(file) |> SQLite.load!(db, "sqlite_table")

Supported keyword arguments include:

• File layout options:
  • header=1: the header argument can be an Int, indicating the row to parse for column names; or a Range, indicating a span of rows to be concatenated together as column names; or an entire Vector{Symbol} or Vector{String} to use as column names; if a file doesn't have column names, either provide them as a Vector, or set header=0 or header=false and column names will be auto-generated (Column1, Column2, etc.)
  • normalizenames=false: whether column names should be "normalized" into valid Julia identifier symbols; useful when iterating rows and accessing column values of a row via getproperty (e.g. row.col1)
  • datarow: an Int argument to specify the row where the data starts in the csv file; by default, the next row after the header row is used. If header=0, then the 1st row is assumed to be the start of data
  • skipto::Int: similar to datarow, specifies the number of rows to skip before starting to read data
  • footerskip::Int: number of rows at the end of a file to skip parsing
  • limit: an Int to indicate a limited number of rows to parse in a csv file; use in combination with skipto to read a specific, contiguous chunk within a file
  • transpose::Bool: read a csv file "transposed", i.e. each column is parsed as a row
  • comment: rows that begin with this String will be skipped while parsing
  • use_mmap::Bool=!Sys.iswindows(): whether the file should be mmapped for reading, which in some cases can be faster
  • ignoreemptylines::Bool=false: whether empty rows/lines in a file should be ignored (if false, each column will be assigned missing for that empty row)
  • threaded::Bool: whether parsing should utilize multiple threads; by default threads are used on large enough files, but isn't allowed when transpose=true or when limit is used; only available in Julia 1.3+
  • select: an AbstractVector of Int, Symbol, String, or Bool, or a "selector" function of the form (i, name) -> keep::Bool; only columns in the collection or for which the selector function returns true will be parsed and accessible in the resulting CSV.File. Invalid values in select are ignored.
  • drop: inverse of select; an AbstractVector of Int, Symbol, String, or Bool, or a "drop" function of the form (i, name) -> drop::Bool; columns in the collection or for which the drop function returns true will ignored in the resulting CSV.File. Invalid values in drop are ignored.
• Parsing options:
  • missingstrings, missingstring: either a String, or Vector{String} to use as sentinel values that will be parsed as missing; by default, only an empty field (two consecutive delimiters) is considered missing
  • delim=',': a Char or String that indicates how columns are delimited in a file; if no argument is provided, parsing will try to detect the most consistent delimiter on the first 10 rows of the file
  • ignorerepeated::Bool=false: whether repeated (consecutive) delimiters should be ignored while parsing; useful for fixed-width files with delimiter padding between cells
  • quotechar='"', openquotechar, closequotechar: a Char (or different start and end characters) that indicate a quoted field which may contain textual delimiters or newline characters
  • escapechar='"': the Char used to escape quote characters in a quoted field
  • dateformat::Union{String, Dates.DateFormat, Nothing}: a date format string to indicate how Date/DateTime columns are formatted for the entire file
  • decimal='.': a Char indicating how decimals are separated in floats, i.e. 3.14 used '.', or 3,14 uses a comma ','
  • truestrings, falsestrings: Vectors of Strings that indicate how true or false values are represented; by default only true and false are treated as Bool
• Column Type Options:
  • type: a single type to use for parsing an entire file; i.e. all columns will be treated as the same type; useful for matrix-like data files
  • types: a Vector or Dict of types to be used for column types; a Dict can map column index Int, or name Symbol or String to type for a column, i.e. Dict(1=>Float64) will set the first column as a Float64, Dict(:column1=>Float64) will set the column named column1 to Float64 and, Dict("column1"=>Float64) will set the column1 to Float64; if a Vector if provided, it must match the # of columns provided or detected in header
  • typemap::Dict{Type, Type}: a mapping of a type that should be replaced in every instance with another type, i.e. Dict(Float64=>String) would change every detected Float64 column to be parsed as String
  • pool::Union{Bool, Float64}=0.1: if true, all columns detected as String will be internally pooled; alternatively, the proportion of unique values below which String columns should be pooled (by default 0.1, meaning that if the # of unique strings in a column is under 10%, it will be pooled)
  • categorical::Bool=false: whether pooled columns should be copied as CategoricalArray instead of PooledArray; note that in CSV.read, by default, columns are not copied, so pooled columns will have type CSV.Column{String, PooledString}; to get CategoricalArray columns, also pass copycols=true
  • strict::Bool=false: whether invalid values should throw a parsing error or be replaced with missing
  • silencewarnings::Bool=false: if strict=false, whether invalid value warnings should be silenced

CSV.read(source; copycols::Bool=false, kwargs...) => DataFrame

Parses a delimited file into a DataFrame. copycols determines whether a copy of columns should be made when creating the DataFrame; by default, no copy is made, and the DataFrame is built with immutable, read-only CSV.Column vectors. If mutable operations are needed on the DataFrame columns, set copycols=true.

CSV.read supports the same keyword arguments as CSV.File.

CSV.Rows(source; kwargs...) => CSV.Rows

Read a csv input (a filename given as a String or FilePaths.jl type, or any other IO source), returning a CSV.Rows object.

While similar to CSV.File, CSV.Rows provides a slightly different interface, the tradeoffs including:

• Very minimal memory footprint; while iterating, only the current row values are buffered
• Only provides row access via iteration; to access columns, one can stream the rows into a table type
• Performs no type inference; each column/cell is essentially treated as Union{String, Missing}, users can utilize the performant Parsers.parse(T, str) to convert values to a more specific type if needed

Opens the file and uses passed arguments to detect the number of columns, ***but not*** column types. The returned CSV.Rows object supports the Tables.jl interface and can iterate rows. Each row object supports propertynames, getproperty, and getindex to access individual row values. Note that duplicate column names will be detected and adjusted to ensure uniqueness (duplicate column name a will become a_1). For example, one could iterate over a csv file with column names a, b, and c by doing:

for row in CSV.Rows(file)
    println("a=$(row.a), b=$(row.b), c=$(row.c)")
end

Supported keyword arguments include:

• File layout options:
  • header=1: the header argument can be an Int, indicating the row to parse for column names; or a Range, indicating a span of rows to be concatenated together as column names; or an entire Vector{Symbol} or Vector{String} to use as column names; if a file doesn't have column names, either provide them as a Vector, or set header=0 or header=false and column names will be auto-generated (Column1, Column2, etc.)
  • normalizenames=false: whether column names should be "normalized" into valid Julia identifier symbols; useful when iterating rows and accessing column values of a row via getproperty (e.g. row.col1)
  • datarow: an Int argument to specify the row where the data starts in the csv file; by default, the next row after the header row is used. If header=0, then the 1st row is assumed to be the start of data
  • skipto::Int: similar to datarow, specifies the number of rows to skip before starting to read data
  • limit: an Int to indicate a limited number of rows to parse in a csv file; use in combination with skipto to read a specific, contiguous chunk within a file
  • transpose::Bool: read a csv file "transposed", i.e. each column is parsed as a row
  • comment: rows that begin with this String will be skipped while parsing
  • use_mmap::Bool=!Sys.iswindows(): whether the file should be mmapped for reading, which in some cases can be faster
  • ignoreemptylines::Bool=false: whether empty rows/lines in a file should be ignored (if false, each column will be assigned missing for that empty row)
• Parsing options:
  • missingstrings, missingstring: either a String, or Vector{String} to use as sentinel values that will be parsed as missing; by default, only an empty field (two consecutive delimiters) is considered missing
  • delim=',': a Char or String that indicates how columns are delimited in a file; if no argument is provided, parsing will try to detect the most consistent delimiter on the first 10 rows of the file
  • ignorerepeated::Bool=false: whether repeated (consecutive) delimiters should be ignored while parsing; useful for fixed-width files with delimiter padding between cells
  • quotechar='"', openquotechar, closequotechar: a Char (or different start and end characters) that indicate a quoted field which may contain textual delimiters or newline characters
  • escapechar='"': the Char used to escape quote characters in a quoted field
  • strict::Bool=false: whether invalid values should throw a parsing error or be replaced with missing
  • silencewarnings::Bool=false: if strict=false, whether warnings should be silenced
• Iteration options:
  • reusebuffer=false: while iterating, whether a single row buffer should be allocated and reused on each iteration; only use if each row will be iterated once and not re-used (e.g. it's not safe to use this option if doing collect(CSV.Rows(file)))

CSV.write(file, table; kwargs...) => file
table |> CSV.write(file; kwargs...) => file

Write a Tables.jl interface input to a csv file, given as an IO argument or String/FilePaths.jl type representing the file name to write to.

Supported keyword arguments include:

• delim::Union{Char, String}=',': a character or string to print out as the file's delimiter
• quotechar::Char='"': ascii character to use for quoting text fields that may contain delimiters or newlines
• openquotechar::Char: instead of quotechar, use openquotechar and closequotechar to support different starting and ending quote characters
• escapechar::Char='"': ascii character used to escape quote characters in a text field
• missingstring::String="": string to print for missing values
• dateformat=Dates.default_format(T): the date format string to use for printing out Date & DateTime columns
• append=false: whether to append writing to an existing file/IO, if true, it will not write column names by default
• writeheader=!append: whether to write an initial row of delimited column names, not written by default if appending
• header: pass a list of column names (Symbols or Strings) to use instead of the column names of the input table
• newline='\n': character or string to use to separate rows (lines in the csv file)
• quotestrings=false: whether to force all strings to be quoted or not
• decimal='.': character to use as the decimal point when writing floating point numbers
• transform=(col,val)->val: a function that is applied to every cell e.g. we can transform all nothing values to missing using (col, val) -> something(val, missing)
• bom=false: whether to write a UTF-8 BOM header (0xEF 0xBB 0xBF) or not