loadarff#
- scipy.io.arff.loadarff(f)[source]#
- 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 - datarecord array like so:- data['pressure'][0:10]- Parameters:
- ffile-like or str
- File-like object to read from, or filename to open. 
 
- Returns:
- datarecord array
- The data of the arff file, accessible by attribute names. 
- metaMetaData
- 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 io 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')