TableReader.jl

TableReader.jl does not waste your time.

Features:

• Carefully optimized for speed.
• Transparently decompresses gzip, xz, and zstd data.
• Read data from a local file, a remote file, or a running process.

Here is a quick benchmark of start-up time:

~/w/TableReader (master|…) $ julia
               _
   _       _ _(_)_     |  Documentation: https://docs.julialang.org
  (_)     | (_) (_)    |
   _ _   _| |_  __ _   |  Type "?" for help, "]?" for Pkg help.
  | | | | | | |/ _` |  |
  | | |_| | | | (_| |  |  Version 1.1.0 (2019-01-21)
 _/ |\__'_|_|_|\__'_|  |  Official https://julialang.org/ release
|__/                   |

julia> using TableReader

julia> @time readcsv("data/iris.csv");  # start-up time
  2.301008 seconds (2.80 M allocations: 139.657 MiB, 1.82% gc time)

~/w/TableReader (master|…) $ julia -q
julia> using CSV, DataFrames

julia> @time DataFrame(CSV.File("data/iris.csv"));  # start-up time
  7.443172 seconds (33.26 M allocations: 1.389 GiB, 9.05% gc time)

~/w/TableReader (master|…) $ julia -q
julia> using CSVFiles, DataFrames

julia> @time DataFrame(load("data/iris.csv"));  # start-up time
 12.578236 seconds (47.81 M allocations: 2.217 GiB, 9.87% gc time)

And the parsing throughput of TableReader.jl is often ~1.5-3.0 times faster than those of pandas and other Julia packages. See this post for more selling points.

Start a new session by the julia command, hit the ] key to change the mode, and run add TableReader in the pkg> prompt.

# This takes the three functions into the current scope:
#   - readdlm
#   - readcsv
#   - readtsv
using TableReader

# Read a CSV file and return a DataFrame object.
dataframe = readcsv("somefile.csv")

# Automatic delimiter detection.
dataframe = readdlm("somefile.txt")

# Read gzip/xz/zstd compressed files.
dataframe = readcsv("somefile.csv.gz")

# Read a remote file as downloading.
dataframe = readcsv("https://example.com/somefile.csv")

# Read stdout from a process.
dataframe = readcsv(`unzip -p data.zip somefile.csv`)

The following parameters are available:

• delim: specify the delimiter character
• quot: specify the quotation character
• trim: trim space around fields
• lzstring: parse excess leading zeros as strings
• skip: skip the leading lines
• skipblank: skip blank lines
• comment: specify the leading sequence of comment lines
• colnames: set the column names
• normalizenames: "normalize" column names into valid Julia (DataFrame) identifier symbols
• hasheader: notify the parser the existence of a header
• chunkbits: set the size of a chunk

See the docstring of readdlm for more details.

TableReader.jl is aimed at users who want to keep the easy things easy. It exports three functions: readdlm, readcsv, and readtsv. readdlm is at the core of the package, and the other two functions are a thin wrapper that calls readdlm with some default parameters; readcsv is for CSV files and readtsv is for TSV files. These functions always return a data frame object of DataFrames.jl. No other functions except the three are exported from this package.

Things happen transparently:

1. The functions detect compression from data so users do not need to specify any parameters to notify the fact.
2. The data types of columns are guessed from data (integers, floats, bools, dates, datetimes, strings, and missings are supported).
3. If the data source looks like a URL, it is downloaded with the curl command.
4. readdlm detects the delimiter of fields from data (of course, you can force a specific delimiter using the delim parameter).

The three functions takes an object as the source of tabular data to read. It may be a filename, a URL string, a command, or any kind of I/O objects. For example, the following examples will work as you expect:

readcsv("path/to/filename.csv")
readcsv("https://example.com/path/to/filename.csv")
readcsv(`unzip -p path/to/dataset.zip filename.csv`)
readcsv(IOBuffer(some_csv_data))

To reduce memory usage, the parser reads data chunk by chunk and the data types are guessed using the buffered data in the first chunk. If the chunk size is not enough to detect the types correctly, the parser will fail when it detects unexpected data fields. You can expand the chunk size by the chunkbits parameter; the default size is chunkbits = 20, which means 2^20 bytes (= 1 MiB). If you set the value to zero (i.e., chunkbits = 0), the parser reads the whole data file into a buffer without chunking it. This theoretically never mistakes the data types in exchange for higher memory usage.

The tokenizer cannot handle extremely long fields in a data file. The length of a token is encoded using 24-bit integer, and therefore a cell that is longer than or equal to 16 MiB will result in parsing failure. This is not likely to happen, but please be careful if, for example, there are columns that contain long strings. Also, the size of a chunk is limited up to 64 GiB; you cannot disable chunking if the data size is larger than that.