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scipy.io.arff.MetaData

# scipy.io.arff.loadarff

`scipy.io.arff.``loadarff`(f)

  Read an arff file.

  The data is returned as a record array, which can be accessed much like a dictionary of numpy arrays. For example, if one of the attributes is called ‘pressure’, then its first 10 data points can be accessed from the `data` record array like so: `data['pressure'][0:10]`

  Parameters:

  f : file-like or str

  > File-like object to read from, or filename to open.

  Returns:

  data : record array

  > The data of the arff file, accessible by attribute names.

  meta : `MetaData`

  > Contains information about the arff file such as name and type of attributes, the relation (name of the dataset), etc…

  Raises:

  ParseArffError

  > This is raised if the given file is not ARFF-formatted.

  NotImplementedError

  > The ARFF file has an attribute which is not supported yet.

  Notes

  This function should be able to read most arff files. Not implemented functionality include:

  - date type attributes
  - string type attributes

  It can read files with numeric and nominal attributes. It cannot read files with sparse data ({} in the file). However, this function can read files with missing data (? in the file), representing the data points as NaNs.

  Examples

      >>> from scipy.io import arff
      >>> from cStringIO import StringIO
      >>> content = """
      ... @relation foo
      ... @attribute width  numeric
      ... @attribute height numeric
      ... @attribute color  {red,green,blue,yellow,black}
      ... @data
      ... 5.0,3.25,blue
      ... 4.5,3.75,green
      ... 3.0,4.00,red
      ... """
      >>> f = StringIO(content)
      >>> data, meta = arff.loadarff(f)
      >>> data
      array([(5.0, 3.25, 'blue'), (4.5, 3.75, 'green'), (3.0, 4.0, 'red')],
            dtype=[('width', '<f8'), ('height', '<f8'), ('color', '|S6')])
      >>> meta
      Dataset: foo
          width's type is numeric
          height's type is numeric
          color's type is nominal, range is ('red', 'green', 'blue', 'yellow', 'black')