tsfresh.utilities package¶

Submodules¶

tsfresh.utilities.dataframe_functions module¶

Utility functions for handling the DataFrame conversions to the internal normalized format (see normalize_input_to_internal_representation) or on how to handle NaN and inf in the DataFrames.

tsfresh.utilities.dataframe_functions.check_for_nans_in_columns(df, columns=None)[source]¶

Helper function to check for NaN in the data frame and raise a ValueError if there is one.

Parameters:	df (pandas.DataFrame) – the pandas DataFrame to test for NaNs columns (list) – a list of columns to test for NaNs. If left empty, all columns of the DataFrame will be tested.
Returns:	None
Return type:	None
Raise:	`ValueError` of `NaNs` are found in the DataFrame.

tsfresh.utilities.dataframe_functions.get_range_values_per_column(df)[source]¶

Retrieves the finite max, min and mean values per column in df and stores them in three dictionaries, each mapping from column name to value. If a column does not contain finite value, 0 is stored instead.

Parameters:	df – `Dataframe`
Returns:	Dictionaries mapping column names to max, min, mean values

tsfresh.utilities.dataframe_functions.impute(df_impute)[source]¶

Columnwise replaces all NaNs and infs from the DataFrame df_impute with average/extreme values from the same columns. This is done as follows: Each occurring inf or NaN in df_impute is replaced by

-inf -> min

+inf -> max

NaN -> median

If the column does not contain finite values at all, it is filled with zeros.

This function modifies df_impute in place. After that, df_impute is guaranteed to not contain any non-finite values. Also, all columns will be guaranteed to be of type np.float64.

Parameters:	df_impute (pandas.DataFrame) – DataFrame to impute
Returns:	None
Return type:	None

tsfresh.utilities.dataframe_functions.impute_dataframe_range(df_impute, col_to_max=None, col_to_min=None, col_to_median=None)[source]¶

Columnwise replaces all NaNs and infs from the DataFrame df_impute with average/extreme values from the provided dictionaries. This is done as follows: Each occurring inf or NaN in df_impute is replaced by

-inf -> min

+inf -> max

NaN -> median

If a column is not found in the one of the dictionaries, the values are calculated from the columns finite values. If the column does not contain finite values at all, it is filled with zeros.

This function modifies df_impute in place. Unless the dictionaries contain NaNs or infs, df_impute is guaranteed to not contain any non-finite values. Also, all columns will be guaranteed to be of type np.float64.

Parameters:	df_impute (pandas.DataFrame) – DataFrame to impute col_to_max (dict) – Dictionary mapping column names to max values col_to_min – Dictionary mapping column names to min values col_to_median – Dictionary mapping column names to median values

tsfresh.utilities.dataframe_functions.impute_dataframe_zero(df_impute)[source]¶

Replaces all NaNs and infs from the DataFrame df_impute with 0s.

df_impute will be modified in place. All its columns will be of datatype np.float64.

Parameters:	df_impute (pandas.DataFrame) – DataFrame to impute

tsfresh.utilities.dataframe_functions.normalize_input_to_internal_representation(df_or_dict, column_id, column_sort, column_kind, column_value)[source]¶

Try to transform any given input to the internal representation of time series, which is a mapping from string (the kind) to a pandas DataFrame with exactly two columns (the value and the id).

This function can transform pandas DataFrames in different formats or dictionaries to pandas DataFrames in different formats. It is used internally in the extract_features function and should not be called by the user.

Parameters:	df_or_dict (pandas.DataFrame or dict) – a pandas DataFrame or a dictionary. The required shape/form of the object depends on the rest of the passed arguments. column_id (basestring or None) – if not None, it must be present in the pandas DataFrame or in all DataFrames in the dictionary. It is not allowed to have NaN values in this column. If this column name is None, a new column will be added to the pandas DataFrame (or all pandas DataFrames in the dictionary) and the same id for all entries is assumed. column_sort (basestring or None) – if not None, sort the rows by this column. Then, the column is dropped. It is not allowed to have NaN values in this column. column_kind (basestring or None) – It can only be used when passing a pandas DataFrame (the dictionary is already assumed to be grouped by the kind). Is must be present in the DataFrame and no NaN values are allowed. The DataFrame will be grouped by the values in the kind column and each grouped will be one entry in the resulting mapping. If the kind column is not passed, it is assumed that each column in the pandas DataFrame (except the id or sort column) is a possible kind and the DataFrame is split up into as many DataFrames as there are columns. Except when a value column is given: then it is assumed that there is only one column. column_value (basestring or None) – If it is given, it must be present and not-NaN on the pandas DataFrames (or all pandas DataFrames in the dictionaries). If it is None, it is assumed that there is only a single remaining column in the DataFrame(s) (otherwise an exception is raised).
Returns:	A tuple of 3 elements: the normalized DataFrame as a dictionary mapping from the kind (as a string) to the corresponding DataFrame, the name of the id column and the name of the value column
Return type:	(dict, basestring, basestring)
Raise:	`ValueError` when the passed combination of parameters is wrong or does not fit to the input DataFrame or dict.

tsfresh.utilities.dataframe_functions.restrict_input_to_index(df_or_dict, column_id, index)[source]¶

Restrict df_or_dict to those ids contained in index.

Parameters:	df_or_dict (pandas.DataFrame or dict) – a pandas DataFrame or a dictionary. column_id (basestring) – it must be present in the pandas DataFrame or in all DataFrames in the dictionary. It is not allowed to have NaN values in this column. index (Iterable or pandas.Series) – Index containing the ids
Returns:	the restricted df_or_dict
Return type:	dict or pandas.DataFrame
Raise:	`TypeError` if df_or_dict is not of type dict or pandas.DataFrame

tsfresh.utilities.profiling module¶

Contains methods to start and stop the profiler that checks the runtime of the different feature calculators

tsfresh.utilities.profiling.end_profiling(profiler, filename, sorting=None)[source]¶

Helper function to stop the profiling process and write out the profiled data into the given filename. Before this, sort the stats by the passed sorting.

Parameters:	profiler (cProfile.Profile) – An already started profiler (probably by start_profiling). filename (basestring) – The name of the output file to save the profile. sorting (basestring) – The sorting of the statistics passed to the sort_stats function.
Returns:	None
Return type:	None

Start and stop the profiler with:

>>> profiler = start_profiling()
>>> # Do something you want to profile
>>> end_profiling(profiler, "out.txt", "cumulative")

tsfresh.utilities.profiling.start_profiling()[source]¶

Helper function to start the profiling process and return the profiler (to close it later).

Returns:	a started profiler.
Return type:	cProfile.Profile

Start and stop the profiler with:

>>> profiler = start_profiling()
>>> # Do something you want to profile
>>> end_profiling(profiler, "cumulative", "out.txt")

Module contents¶

This utilities submodule contains several utility functions. Those should only be used internally inside tsfresh.