iopro.loadtxt

Load data from a text file.

Each row in the text file must have the same number of values.

Parameters

fname
: file or str
File, filename, or generator to read. If the filename extension is .gz or .bz2, the file is first decompressed. Note that generators should return byte strings for Python 3k.
dtype
: data-type, optional
Data-type of the resulting array; default: float. If this is a record data-type, the resulting array will be 1-dimensional, and each row will be interpreted as an element of the array. In this case, the number of columns used must match the number of fields in the data-type.
comments
: str, optional
The character used to indicate the start of a comment; default: ‘#’.
delimiter
: str, optional
The string used to separate values. By default, this is any whitespace.
converters
: dict, optional
A dictionary mapping column number to a function that will convert that column to a float. E.g., if column 0 is a date string: converters = {0: datestr2num}. Converters can also be used to provide a default value for missing data (but see also iopro.genfromtxt): converters = {3: lambda s: float(s.strip() or 0)}. Default: None.
skiprows
: int, optional
Skip the first skiprows lines; default: 0.
usecols
: sequence, optional
Which columns to read, with 0 being the first. For example, usecols = (1,4,5) will extract the 2nd, 5th and 6th columns. The default, None, results in all columns being read.
unpack
: bool, optional
If True, the returned array is transposed, so that arguments may be unpacked using x, y, z = iopro.loadtxt(...). When used with a record data-type, arrays are returned for each field. Default is False.
ndmin
: int, optional
The returned array will have at least ndmin dimensions. Otherwise mono-dimensional axes will be squeezed. Legal values: 0 (default), 1 or 2. .. versionadded:: 1.6.0

Returns

out
: ndarray
Data read from the text file.

See Also

iopro.genfromtxt : Load data with missing values handled as specified.

Examples

simple parse of StringIO object data
>>> import iopro
>>> from io import StringIO   # StringIO behaves like a file object
>>> c = StringIO("0 1\\n2 3")
>>> iopro.loadtxt(c)
>>> array([[ 0.,  1.],
       [ 2.,  3.]])
set dtype of output array
>>> d = StringIO("M 21 72\\nF 35 58")
>>> iopro.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
...                      'formats': ('S1', 'i4', 'f4')})
>>> array([('M', 21, 72.0), ('F', 35, 58.0)],
      dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])
set delimiter and columns to parse
>>> c = StringIO("1,0,2\\n3,0,4")
>>> x, y = iopro.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)
>>> x
>>> array([ 1.,  3.])
>>> y
>>> array([ 2.,  4.])