pipelog package¶

Submodules¶

pipelog.custom_agg_funcs module¶

class pipelog.custom_agg_funcs.CustomAggFuncs(value)[source]¶

Bases: enum.Enum

An enumeration.

nans = <function nans_func>¶

notnans = <function not_nans_func>¶

class pipelog.custom_agg_funcs.EnumFunc(func: callable)[source]¶

Bases: object

Wrapper class that enables usage and proper representation for functions in Enums.

pipelog.custom_agg_funcs.nans_func(df: pandas.core.frame.DataFrame) → pandas.core.series.Series[source]¶: Counts the number of nan values for all columns.

pipelog.custom_agg_funcs.not_nans_func(df: pandas.core.frame.DataFrame) → pandas.core.series.Series[source]¶: Counts the number of nan values for all columns.

pipelog.frame_log module¶

class pipelog.frame_log.FrameLog(agg: Optional[pandas.core.frame.DataFrame] = None, agg_axis: Optional[int] = None, dtypes: Optional[dict] = None, shape: Optional[Tuple[int, int]] = None, column_names: Optional[list] = None, copy: Optional[pandas.core.frame.DataFrame] = None)[source]¶: Bases: object

class pipelog.frame_log.FrameLogCollection(*args, **kwargs)[source]¶

Bases: collections.OrderedDict

An OrderedDict, which supports slicing, integer access and some custom functionality.

agg(agg_func_first: bool = False) → pandas.core.frame.DataFrame[source]¶: View agg values as a multi index DataFrame.

append(value: pipelog.frame_log.FrameLog, key: Optional[str] = None) → str[source]¶: Append new entry. If key is not given a new one will be created based on the internal assigment counter.

column_names() → pandas.core.frame.DataFrame[source]¶: View shape values as a DataFrame.

dtypes() → pandas.core.frame.DataFrame[source]¶: View dtypes values as a DataFrame.

shape() → pandas.core.frame.DataFrame[source]¶: View shape values as a DataFrame.

pipelog.pipe_tracker module¶

class pipelog.pipe_tracker.PipeLogger(indices: Optional[list] = None, columns: Optional[list] = None, agg_func: Optional[Union[callable, str, list, dict]] = None, axis: int = 0, dtypes: Optional[bool] = None, shape: Optional[bool] = None, column_names: Optional[bool] = None, copy: Optional[bool] = None)[source]¶

Bases: object

log_frame(df: pandas.core.frame.DataFrame, key: Optional[str] = None, indices: Optional[list] = None, columns: Optional[list] = None, agg_func: Optional[Union[callable, str, list, dict]] = None, agg_axis: int = 0, dtypes: Optional[bool] = None, shape: Optional[bool] = None, column_names: Optional[bool] = None, copy: Optional[bool] = None, return_result: Optional[bool] = None) → None[source]¶: Append frame statistics to the frame_logs depending on the given arguments.

reset() → None[source]¶: Reset all variables that can be set during tracking.

track() → callable[source]¶: Returns a decorator to be used for tracking the input and output of a function.

Module contents¶

Top-level package for pipelog.

class pipelog.PipeLogger(indices: Optional[list] = None, columns: Optional[list] = None, agg_func: Optional[Union[callable, str, list, dict]] = None, axis: int = 0, dtypes: Optional[bool] = None, shape: Optional[bool] = None, column_names: Optional[bool] = None, copy: Optional[bool] = None)[source]¶

Bases: object

log_frame(df: pandas.core.frame.DataFrame, key: Optional[str] = None, indices: Optional[list] = None, columns: Optional[list] = None, agg_func: Optional[Union[callable, str, list, dict]] = None, agg_axis: int = 0, dtypes: Optional[bool] = None, shape: Optional[bool] = None, column_names: Optional[bool] = None, copy: Optional[bool] = None, return_result: Optional[bool] = None) → None[source]¶: Append frame statistics to the frame_logs depending on the given arguments.

reset() → None[source]¶: Reset all variables that can be set during tracking.

track() → callable[source]¶: Returns a decorator to be used for tracking the input and output of a function.