load_from_arff_to_dataframe#
- 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.
- Parameters:
- 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.
- Returns:
- 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.
- DataFrame
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.