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Description
Looking at the CSV Documentation in 1.6, I get this big block instead of a nice list
• 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.). Note that if a row number header and comment or ignoreemtpylines are provided, the header row will be the first
non-commented/non-empty row after the row number, meaning if the provided row number is a commented row, the header row will actually be the
next non-commented row. • 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; providing a datarow or skipto argument does not affect the header argument. Note that if a row number datarow and comment or
ignoreemtpylines are provided, the data row will be the first non-commented/non-empty row after the row number, meaning if the provided row
number is a commented row, the data row will actually be the next non-commented row. • skipto::Int: identical 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. Do note that
commented rows (see the comment keyword argument) do not count towards the row number provided for footerskip, they are completely ignored by
the parser • 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; note for large files when multiple threads are used for parsing, the limit argument may not result in exact
an exact # of rows parsed; use threaded=false to ensure an exact limit if necessary • 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. Note that if a row number header or
datarow and comment are provided, the header/data row will be the first non-commented/non-empty row after the row number, meaning if the
provided row number is a commented row, the header/data row will actually be the next non-commented row. • ignoreemptylines::Bool=true:
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;
only available in Julia 1.3+ • tasks::Integer=Threads.nthreads(): for multithreaded parsing, this controls the number of tasks spawned to
read a file in chunks concurrently; defaults to the # of threads Julia was started with (i.e. JULIA_NUM_THREADS environment variable) •
lines_to_check::Integer=5: for multithreaded parsing, a file is split up into tasks # of equal chunks, then lines_to_check # of lines are
checked to ensure parsing correctly found valid rows; for certain files with very large quoted text fields, lines_to_check may need to be
higher (10, 30, etc.) to ensure parsing correctly finds these rows • 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.
Will have to check if this problem on master.