read_fwf(filepath_or_buffer: 'FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str]', colspecs: 'list[tuple[int, int]] | str | None' = 'infer', widths: 'list[int] | None' = None, infer_nrows: 'int' = 100, **kwds) -> 'DataFrame | TextFileReader'
Also supports optionally iterating or breaking of the file into chunks.
Additional help can be found in the :None:None:`online docs for IO Tools
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`.
String, path object (implementing os.PathLike[str]
), or file-like object implementing a text read()
function.The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: file://localhost/path/to/table.csv
.
A list of tuples giving the extents of the fixed-width fields of each line as half-open intervals (i.e., [from, to[ ). String value 'infer' can be used to instruct the parser to try detecting the column specifications from the first 100 rows of the data which are not being skipped via skiprows (default='infer').
A list of field widths which can be used instead of 'colspecs' if the intervals are contiguous.
The number of rows to consider when letting the parser determine the colspecs
.
Optional keyword arguments can be passed to TextFileReader
.
A comma-separated values (csv) file is returned as two-dimensional data structure with labeled axes.
Read a table of fixed-width formatted lines into DataFrame.
DataFrame.to_csv
Write DataFrame to a comma-separated values (csv) file.
read_csv
Read a comma-separated values (csv) file into DataFrame.
>>> pd.read_fwf('data.csv') # doctest: +SKIPSee :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.io.excel._base.read_excel
pandas.io.parsers.readers.read_csv
pandas.io.parsers.readers.read_table
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