load_table#
- load_table(filename: str | Path, sep: str | None = None, reader: Callable | None = None, digits: int = 4, space: int = 4, title: str = '', missing_data: str = '', max_width: int = 10000000000000000159028911097599180468360808563945281389781327557747838772170381060813469985856815104, index_name: str | None = None, legend: str = '', column_templates: dict | None = None, static_column_types: bool = False, limit: int | None = None, format_name: str = 'simple', skip_inconsistent: bool = False, **kwargs) Table#
- Parameters:
- filename
path to file containing a tabular data
- sep
the delimiting character between columns
- reader
a parser for reading filename. This approach assumes the first row returned by the reader will be the header row.
- static_column_types
if True, and reader is None, identifies columns with a numeric/bool data types from the first non-header row. This assumes all subsequent entries in that column are of the same type. Default is False.
- digits
floating point resolution
- space
number of spaces between columns or a string
- title
as implied
- missing_data
character assigned if a row has no entry for a column
- max_width
maximum column width for printing
- index_name
column name with values to be used as row identifiers and keys for slicing. All column values must be unique.
- legend
table legend
- column_templates
dict of column headings or a function that will handle the formatting.
- limit
exits after this many lines. Only applied for non pickled data file types.
- format_name
output format when using str(Table)
- skip_inconsistent
skips rows that have different length to header row