Continuum Analytics: Documentation

# 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.])