load_from_arff_to_dataframe(full_file_path_and_name, has_class_labels=True, return_separate_X_and_y=True, replace_missing_vals_with='NaN')[source]#

Load data from a .arff file into a Pandas DataFrame.

full_file_path_and_name: str

The full pathname of the .arff file to read.

has_class_labels: bool

true then line contains separated strings and class value contains list of separated strings, check for ‘return_separate_X_and_y’ false otherwise.

return_separate_X_and_y: bool

true then X and Y values should be returned as separate Data Frames ( X) and a numpy array (y), false otherwise. This is only relevant for data.

replace_missing_vals_with: str

The value that missing values in the text file should be replaced with prior to parsing.

DataFrame, ndarray

If return_separate_X_and_y then a tuple containing a DataFrame and a numpy array containing the relevant time-series and corresponding class values.


If not return_separate_X_and_y then a single DataFrame containing all time-series and (if relevant) a column “class_vals” the associated class values.