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io.jl
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io.jl
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# unit tests of extract_string
x = "12345678"
@assert DataFrames.extract_string(x, 3, 6) == "3456"
@assert DataFrames.extract_string(x, 3, 3) == "3"
@assert DataFrames.extract_string(x, 3, 6, Set(3)) == "456"
@assert DataFrames.extract_string(x, 3, 6, Set(5, 3)) == "46"
x = "\"Güerín\",\"Sí\",\"No\""
@assert DataFrames.extract_string(x, 2, 7, Set(3)) == "Gerí"
@assert DataFrames.extract_string("", 0,0,Set()) == ""
# Handle the empty string properly
test0 = IOString("")
res0 = DataFrames.read_separated_line(test0, ',', '"')
@assert isempty(res0)
test1 = IOString("I'm A,I'm B,I'm C,-0.3932755625236671,20.157657978753534")
res1 = DataFrames.read_separated_line(test1, ',', '"')
@assert res1[2] == "I'm B"
@assert res1[4] == "-0.3932755625236671"
test2 = IOString("123, 456 , \"789\",TRUE")
res2 = DataFrames.read_separated_line(test2, ',', '"')
@assert res2[2] == "456"
@assert res2[3] == "789"
test3 = IOString("123 ,456 , \"789\" , \"TRUE\"")
res3 = DataFrames.read_separated_line(test3, ',', '"')
@assert res3[2] == "456"
@assert res3[4] == "TRUE"
test4 = IOString("123 ,456 , , \"TRUE\"")
res4 = DataFrames.read_separated_line(test4, ',', '"')
@assert res4[3] == ""
@assert res4[4] == "TRUE"
test5 = IOString("123 ,456 , \"a\"\"b\" ,\"TRUE\"")
res5 = DataFrames.read_separated_line(test5, ',', '"')
@assert res5[3] == "a\"b"
@assert res5[4] == "TRUE"
test6 = IOString("123 ,456 , \"a
b\" ,\"TRUE\"")
res6 = DataFrames.read_separated_line(test6, ',', '"')
@assert res6[3] == "a\nb"
@assert res6[4] == "TRUE"
# Should this be one NA?
# test7 = IOString("")
# res7 = DataFrames.read_separated_line(test7, ',', '"')
# @assert length(res7) == 1
test8 = IOString("a,\"b\",\"cd\",1.0,1\na,\"b\",\"cd\",1.0,1")
res8 = DataFrames.read_separated_line(test8, ',', '"')
@assert length(res8) == 5
@assert res8[5] == "1"
test9 = IOString("\"Güerín\",\"Sí\",\"No\"")
res9 = DataFrames.read_separated_line(test9, ',', '"')
@assert res9[2] == "Sí"
test10 = IOString("1,2,3,,")
res10 = DataFrames.read_separated_line(test10, ',', '"')
@assert length(res10) == 5
filename = Pkg.dir("DataFrames", "test", "data", "simple_data.csv")
open(filename,"r") do io
t1 = read_table(io, ',', '"', DataFrames.DEFAULT_MISSINGNESS_INDICATORS, false, ["1","2","3","4","5"], 2)
@assert nrow(t1) == 2
@assert t1[1,2] == "b"
end
# Test separated line splitting
#
# TODO: Test minimially-quoted
# TODO: Test only-strings-quoted
separators = [',', '\t', ' ']
quotation_characters = ['\'', '"']
# Test all-entries-quoted for all quote characters and separators
items = {"a", "b", "c,d", "1.0", "1"}
item_buffer = Array(UTF8String, length(items))
# TODO: make this work with new splitting code
# for separator in separators
# for quotation_character in quotation_characters
# line = join(map(x -> string(quotation_character, x, quotation_character),
# items),
# separator)
# current_item_buffer = Array(Char, strlen(line))
# split_results = DataFrames.split_separated_line(line, separator, quotation_character, item_buffer, current_item_buffer)
# @assert all(split_results .== items)
# end
# end
# Test reading
@assert DataFrames.determine_separator("blah.csv") == ','
@assert DataFrames.determine_separator("blah.tsv") == '\t'
@assert DataFrames.determine_separator("blah.wsv") == ' '
# @assert DataFrames.determine_separator("blah.txt")
# Need to change to use @expects to test that error gets raised
filename = Pkg.dir("DataFrames", "test", "data", "big_data.csv")
separator = DataFrames.determine_separator(filename)
quotation_character = '"'
missingness_indicators = ["", "NA"]
header = true
column_names = UTF8String["A", "B", "C", "D", "E"]
minibatch_size = 10
file = open(filename, "r")
readline(file)
minibatch = read_minibatch(file,
separator,
quotation_character,
missingness_indicators,
column_names,
minibatch_size)
@assert nrow(minibatch) == minibatch_size
@assert ncol(minibatch) == length(column_names)
@assert colnames(minibatch) == column_names
@assert eltype(minibatch[:, 1]) == UTF8String
@assert eltype(minibatch[:, 2]) == UTF8String
@assert eltype(minibatch[:, 3]) == UTF8String
@assert eltype(minibatch[:, 4]) == Float64
@assert eltype(minibatch[:, 5]) == Float64
close(file)
@elapsed df = read_table(filename)
@assert nrow(df) == 10_000
@assert ncol(df) == 5
@assert colnames(df) == column_names
@assert typeof(df[:, 1]) == DataVector{UTF8String}
@assert typeof(df[:, 2]) == DataVector{UTF8String}
@assert typeof(df[:, 3]) == DataVector{UTF8String}
@assert typeof(df[:, 4]) == DataVector{Float64}
@assert typeof(df[:, 5]) == DataVector{Float64}
# TODO: Split apart methods that perform seek() from those that don't
text_data = convert(Array{UTF8String, 2}, (["1" "3" "A"; "2" "3" "NA"; "3" "3.1" "C"]))
true_df = DataFrame(quote
x1 = DataArray([1, 2, 3])
x2 = DataArray([3, 3, 3.1])
x3 = DataArray(UTF8String["A", "", "C"], [false, true, false])
end)
df = DataFrames.convert_to_dataframe(text_data,
["", "NA"],
["x1", "x2", "x3"])
@assert isequal(df, true_df)
@assert isequal(eltype(df["x1"]), Int64)
@assert isequal(eltype(df["x2"]), Float64)
@assert isequal(eltype(df["x3"]), UTF8String)
filename = Pkg.dir("DataFrames", "test", "data", "big_data.csv")
separator = DataFrames.determine_separator(filename)
quotation_character = '"'
missingness_indicators = ["", "NA"]
header = true
nrows = DataFrames.determine_nrows(filename, header)
@assert nrows == 10_000
io = open(filename, "r")
column_names = DataFrames.determine_column_names(io, separator, quotation_character, header)
@assert column_names == UTF8String["A", "B", "C", "D", "E"]
seek(io, 0)
if header
readline(io)
end
text_data = DataFrames.read_separated_text(io, nrows, separator, quotation_character)
@assert eltype(text_data) == UTF8String
@assert size(text_data) == (10_000, 5)
df = read_table(io,
separator,
quotation_character,
missingness_indicators,
header,
column_names,
nrows)
@assert nrow(df) == 10_000
@assert ncol(df) == 5
@assert colnames(df) == column_names
@assert eltype(df[:, 1]) == UTF8String
@assert eltype(df[:, 2]) == UTF8String
@assert eltype(df[:, 3]) == UTF8String
@assert eltype(df[:, 4]) == Float64
@assert eltype(df[:, 5]) == Float64
df = read_table(filename)
@assert nrow(df) == 10_000
@assert ncol(df) == 5
@assert colnames(df) == column_names
@assert eltype(df[:, 1]) == UTF8String
@assert eltype(df[:, 2]) == UTF8String
@assert eltype(df[:, 3]) == UTF8String
@assert eltype(df[:, 4]) == Float64
@assert eltype(df[:, 5]) == Float64
# TODO: Add test case in which data file has header, but no rows
# Example "RDatasets/data/Zelig/sna.ex.csv"
# "","Var1","Var2","Var3","Var4","Var5"
# Additional data sets
@elapsed df = read_table("test/data/big_data.csv")
# TODO: Make this faster
@elapsed df = read_table("test/data/movies.csv")
# TODO: Release this data set publicly
#@elapsed df = read_table("test/data/bigrams.tsv")
@elapsed df = read_table("test/data/utf8.csv")
@elapsed df = read_table("test/data/bool.csv")
@elapsed df = read_table("test/data/types.csv")
@elapsed df = read_table("test/data/space_after_delimiter.csv")
@elapsed df = read_table("test/data/space_before_delimiter.csv")
@elapsed df = read_table("test/data/space_around_delimiter.csv")
@elapsed df = read_table("test/data/corrupt_utf8.csv")