文件列表:

LICENSE.md (1198, 2023-08-10)
Project.toml (1204, 2023-08-10)
docs (0, 2023-08-10)
docs\Project.toml (104, 2023-08-10)
docs\make.jl (460, 2023-08-10)
docs\src (0, 2023-08-10)
docs\src\examples.md (23083, 2023-08-10)
docs\src\index.md (6041, 2023-08-10)
docs\src\reading.md (26598, 2023-08-10)
docs\src\writing.md (48, 2023-08-10)
precompile.jl (767, 2023-08-10)
src (0, 2023-08-10)
src\CSV.jl (4435, 2023-08-10)
src\chunks.jl (4978, 2023-08-10)
src\context.jl (31569, 2023-08-10)
src\detection.jl (22476, 2023-08-10)
src\file.jl (46366, 2023-08-10)
src\keyworddocs.jl (12529, 2023-08-10)
src\precompile.jl (676, 2023-08-10)
src\promotions.csv (270111, 2023-08-10)
src\rows.jl (14498, 2023-08-10)
src\utils.jl (25734, 2023-08-10)
src\workload.jl (929, 2023-08-10)
src\write.jl (21236, 2023-08-10)
test (0, 2023-08-10)
test\basics.jl (28449, 2023-08-10)
test\iteration.jl (21575, 2023-08-10)
test\out.test.csv (0, 2023-08-10)
test\perf_write.jl (1125, 2023-08-10)
test\runtests.jl (20753, 2023-08-10)
test\testfiles.jl (64417, 2023-08-10)
test\testfiles (0, 2023-08-10)
... ...

# CSV [](https://github.com/JuliaData/CSV.jl/actions?query=workflow%3ACI) [](https://codecov.io/gh/JuliaData/CSV.jl) [](https://juliahub.com/ui/Packages/CSV/HHBkp?t=2) [](https://juliahub.com/ui/Packages/CSV/HHBkp) [](https://juliahub.com/ui/Packages/CSV/HHBkp) *A fast, flexible delimited file reader/writer for Julia.* ## Installation The package is registered in the [`General`](https://github.com/JuliaRegistries/General) registry and so can be installed at the REPL with `] add CSV`. ## Documentation - [**STABLE**][docs-stable-url] — **most recently tagged version of the documentation.** - [**LATEST**][docs-latest-url] — *in-development version of the documentation.* ## Project Status The package is tested against Julia `1.0`, current stable release, and nightly on Linux, OS X, and Windows. ## Contributing and Questions Contributions are very welcome, as are feature requests and suggestions. Please open an [issue][issues-url] if you encounter any problems or would just like to ask a question. [docs-latest-img]: https://img.shields.io/badge/docs-latest-blue.svg [docs-latest-url]: https://JuliaData.github.io/CSV.jl/latest [docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg [docs-stable-url]: https://JuliaData.github.io/CSV.jl/stable [ci-img]: https://github.com/JuliaData/CSV.jl/workflows/CI/badge.svg [ci-url]: https://github.com/JuliaData/CSV.jl/actions?query=workflow%3ACI+branch%3Amaster [codecov-img]: https://codecov.io/gh/JuliaData/CSV.jl/branch/master/graph/badge.svg [codecov-url]: https://codecov.io/gh/JuliaData/CSV.jl [issues-url]: https://github.com/JuliaData/CSV.jl/issues ## Alternatives There are several other packages for reading CSV files in Julia, which may suit your needs better: * The standard library contains [DelimitedFiles.jl](https://docs.julialang.org/en/v1/stdlib/DelimitedFiles/), at least until Julia 1.8. This returns a `Matrix` rather than a [Tables.jl](https://github.com/JuliaData/Tables.jl)-style container, thus works best for files of homogeneous element type. On large files, CSV.jl will be much faster. * [CSVFiles.jl](https://github.com/queryverse/CSVFiles.jl) uses the [FileIO.jl](https://github.com/JuliaIO/FileIO.jl)'s `load` / `save` API, but otherwise has similar goals. Like CSV.jl, it works with [Tables.jl](https://github.com/JuliaData/Tables.jl) objects such as DataFrames. * [DLMReader.jl](https://github.com/sl-solution/DLMReader.jl) also aims to be fast for large files, closely associated with [InMemoryDatasets.jl](https://github.com/sl-solution/InMemoryDatasets.jl). * [Pandas.jl](https://github.com/JuliaPy/Pandas.jl) wraps Python's [pandas](https://pandas.pydata.org) library (using [PyCall.jl](https://github.com/JuliaPy/PyCall.jl)). This is a closer cousin of [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl), but builds in the ability to read/write CSV files.

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