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')