gsplot.read#

read(path: str | PathLike[str], *, loader: Literal['genfromtxt', 'loadtxt'] = 'genfromtxt', delimiter: str | None = ',', comments: str | None = '#', skip_header: int = 0, usecols: int | Sequence[int] | None = None, unpack: Literal[False], ndmin: Literal[0, 1, 2] = 1, dtype: DTypeLike = float) → ndarray[tuple[Any, ...], dtype[Any]]#
read(path: str | PathLike[str], *, loader: Literal['genfromtxt', 'loadtxt'] = 'genfromtxt', delimiter: str | None = ',', comments: str | None = '#', skip_header: int = 0, usecols: int | Sequence[int] | None = None, unpack: bool = True, ndmin: Literal[0, 1, 2] = 1, dtype: DTypeLike = float) → Any

Read comma-delimited columns with finite NumPy text options.

Parameters:

  • path (str | PathLike[str]) – Explicit text file path. The current working directory is unchanged.

  • loader (Literal['genfromtxt', 'loadtxt']) – NumPy loader name: "genfromtxt" or "loadtxt".

  • delimiter (str | None) – Field delimiter, defaulting to , for CSV. Use None for whitespace-delimited input.

  • comments (str | None) – Comment marker, defaulting to "#". Use None to disable it.

  • skip_header (int) – Non-negative number of initial lines to skip, defaulting to 0.

  • usecols (int | Sequence[int] | None) – Optional integer column or finite integer column sequence.

  • unpack (bool) – Return columns or structured fields separately, defaulting to True.

  • ndmin (Literal[0, 1, 2]) – Minimum result dimensions: 0, 1 (default), or 2.

  • dtype (DTypeLike) – NumPy-compatible dtype, defaulting to float.

Returns:

Native NumPy loader result. Structured unpacking returns a list of per-field arrays and preserves their individual dtypes.

Return type:

numpy.ndarray or list of numpy.ndarray

Raises:

DataError – If a control, path, dtype, or file content is invalid.

Notes

skip_header maps to skiprows for loadtxt. Use gsplot.read_array() for less common NumPy loader options.

Examples

>>> from pathlib import Path
>>> from tempfile import TemporaryDirectory
>>> import gsplot as gs
>>> with TemporaryDirectory() as directory:
...     path = Path(directory) / "data.csv"
...     _ = path.write_text("x,y,z\n0,1,2\n3,4,5\n", encoding="utf-8")
...     columns = gs.read(path, skip_header=1, usecols=(0, 2))
>>> columns.shape
(2, 2)