larray.read_csv¶

larray.read_csv(filepath_or_buffer, nb_index=None, index_col=None, sep=', ', headersep=None, fill_value=nan, na=nan, sort_rows=False, sort_columns=False, dialect='larray', **kwargs)[source]¶

Reads csv file and returns an array with the contents.

Parameters:

filepath_or_buffer : str or any file-like object

Path where the csv file has to be read or a file handle.

nb_index : int, optional

Number of leading index columns (ex. 4).

index_col : list, optional

List of columns for the index (ex. [0, 1, 2, 3]).

sep : str, optional

Separator.

headersep : str or None, optional

Separator for headers.

fill_value : scalar or LArray, optional

Value used to fill cells corresponding to label combinations which are not present in the input. Defaults to NaN.

sort_rows : bool, optional

Whether or not to sort the rows alphabetically (sorting is more efficient than not sorting). Defaults to False.

sort_columns : bool, optional

Whether or not to sort the columns alphabetically (sorting is more efficient than not sorting). Defaults to False.

dialect : ‘classic’ | ‘larray’ | ‘liam2’, optional

Name of dialect. Defaults to ‘larray’.

**kwargs

Returns:

LArray

Notes

csv file format: arr,ages,sex,nat ime,1991,1992,1993 A1,BI,H,BE,1,0,0 A1,BI,H,FO,2,0,0 A1,BI,F,BE,0,0,1 A1,BI,F,FO,0,0,0 A1,A0,H,BE,0,0,0

Examples

>>> from larray.tests.common import abspath
>>> from larray import ndrange
>>> fpath = abspath('test.csv')
>>> a = ndrange('nat=BE,FO;sex=M,F')

>>> a.to_csv(fpath)
>>> read_csv(fpath)
nat\sex  M  F
     BE  0  1
     FO  2  3
>>> read_csv(fpath, sort_columns=True)
nat\sex  F  M
     BE  1  0
     FO  3  2
>>> fpath = abspath('no_axis_name.csv')
>>> a.to_csv(fpath, dialect='classic')
>>> read_csv(fpath, nb_index=1)
nat\{1}  M  F
     BE  0  1
     FO  2  3